Taghi M. Khoshgoftaar

655 papers A* 4A 20B 57C 80Misc 144Journal 225Unranked 118

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Artificial Intelligence and Machine Learning for Job Hazard Analysis: A Survey. IEEE Access Misbah Khan, Preston Billion Polak, Taghi M. Khoshgoftaar
2025	—	`conf`	A Comparison of Rank-Ordering and Classification for Occupational Injury Absence Duration. CIC Chelsea M. Zuvieta, Gonzalo A. Vivian, Taghi M. Khoshgoftaar
2025	Misc	`conf`	A New and Effective Technique for Unsupervised Labeling and Feature Selection with Applications in Healthcare Fraud Detection. IRI John T. Hancock, Robert K. L. Kennedy, Mary Anne Walauskis, Taghi M. Khoshgoftaar
2025	B	`conf`	A Novel Technique to Rank-Order Occupational Injury Claims for Return-to-Work Prediction. ICTAI Gonzalo A. Vivian, Chelsea M. Zuvieta, Taghi M. Khoshgoftaar
2025	J	`jnl`	A Survey on Statistical and ML-Based Demand Forecasting Methods for Spare Parts in Aviation. IEEE Access Chelsea M. Zuvieta, Joffrey L. Leevy, Taghi M. Khoshgoftaar
2025	J	`jnl`	A comprehensive survey on machine learning for workplace injury analysis: risk prediction, return to work strategies, and demographic insights. J. Big Data Gonzalo A. Vivian, Richard A. Bauder, Taghi M. Khoshgoftaar
2025	J	`jnl`	A novel approach to automating unsupervised estimation of class distribution. J. Big Data Mary Anne Walauskis, Taghi M. Khoshgoftaar
2025	J	`jnl`	A problem-agnostic approach to feature selection and analysis using SHAP. J. Big Data John T. Hancock, Taghi M. Khoshgoftaar, Qianxin Liang
2025	J	`jnl`	A review of distinct machine learning classifiers for healthcare fraud detection. J. Big Data Ethan D. Curtis, Preston Billion Polak, Taghi M. Khoshgoftaar, Borko Furht
2025	—	`conf`	An Interpretable and Efficient Random Undersampling-enhanced SHAP Framework for Medicare Fraud Detection. CIC Qianxin Liang, Richard A. Bauder, Taghi M. Khoshgoftaar
2025	C	`conf`	Applying Machine Learning to Classify Automobile Repossession Success. ICMLA Preston Billion Polak, Andy Sinclair, Taghi M. Khoshgoftaar
2025	Misc	`conf`	Choosing the Right Metrics: A Study of Performance Measurement for Binary Classification in Imbalanced and Big Data. FLAIRS Mary Anne Walauskis, Taghi M. Khoshgoftaar
2025	J	`jnl`	Data science, big data, and machine learning are coming of age. J. Big Data Borko Furht, Taghi M. Khoshgoftaar
2025	J	`jnl`	Evaluating techniques from low-shot learning on traditional imbalanced classification tasks. J. Big Data Preston Billion Polak, Taghi M. Khoshgoftaar
2025	J	`jnl`	Evaluating the Impact of Majority versus Minority Class Training for One-Class Classification. Int. J. Artif. Intell. Tools Joffrey L. Leevy, John T. Hancock, Taghi M. Khoshgoftaar, Azadeh Abdollah Zadeh
2025	B	`conf`	Examining the Impact of Feature Selection for Contrastive Learning in Fraud Detection. ICTAI Preston Billion Polak, Taghi M. Khoshgoftaar
2025	C	`conf`	Medical Imaging with Deep Learning: A Comparison of CNN and Transformer Models. ICMLA Kehan Gao, Sarah Tasneem, Taghi M. Khoshgoftaar
2025	C	`conf`	Return-to-Work Classification of Occupational Injury Claims via Rank-Ordering. ICMLA Gonzalo A. Vivian, Chelsea M. Zuvieta, Taghi M. Khoshgoftaar
2025	J	`jnl`	SHAP as a Data Reduction Technique for Highly Imbalanced Big Data. Int. J. Artif. Intell. Tools John T. Hancock, Richard A. Bauder, Taghi M. Khoshgoftaar
2025	J	`jnl`	SHAP-Based Feature Selection for Enhanced Unsupervised Labeling. IEEE Access Mary Anne Walauskis, Taghi M. Khoshgoftaar
2025	J	`jnl`	Scalable unsupervised labeling with SHAP feature selection for fraud detection in imbalanced data. J. Big Data Mary Anne Walauskis, Taghi M. Khoshgoftaar
2025	Misc	`conf`	The Impact of Class Imbalance on Unsupervised Deep Anomaly Detection for Cognitive Data. IRI Zahra Salekshahrezaee, Taghi M. Khoshgoftaar
2025	—	`conf`	Unlocking Machine Learning Insights into a Novel Auto Repossession Dataset. CogMI Andy Sinclair, Preston Billion Polak, Taghi M. Khoshgoftaar
2025	Misc	`conf`	Unsupervised Cognitive Impairment Detection Using Convolutional Autoencoders and Isolation Forest. IRI Zahra Salekshahrezaee, Taghi M. Khoshgoftaar
2025	C	`conf`	Unsupervised Feature Extraction using Convolutional Autoencoder for Credit Card Fraud Detection. ICMLA Zahra Salekshahrezaee, Mary Anne Walauskis, Taghi M. Khoshgoftaar
2025	J	`jnl`	Unsupervised feature selection and class labeling for credit card fraud. J. Big Data Robert K. L. Kennedy, Flavio Villanustre, Taghi M. Khoshgoftaar
2025	J	`jnl`	Unsupervised label generation for severely imbalanced fraud data. J. Big Data Mary Anne Walauskis, Taghi M. Khoshgoftaar
2024	B	`conf`	A Comparison of Low-Shot Learning Methods for Imbalanced Binary Classification. ICTAI Preston Billion Polak, Taghi M. Khoshgoftaar
2024	—	`conf`	A Review of Unsupervised Anomaly Detection Techniques for Health Insurance Fraud. BigDataService Joffrey L. Leevy, Zahra Salekshahrezaee, Taghi M. Khoshgoftaar
2024	J	`jnl`	Advancing machine learning with OCR2SEQ: an innovative approach to multi-modal data augmentation. J. Big Data Michael Lowe, Joseph D. Prusa, Joffrey L. Leevy, Taghi M. Khoshgoftaar
2024	C	`conf`	An Evaluation of Low-Shot Learning Techniques for the Detection of Credit Card Fraud. ICMLA Preston Billion Polak, Taghi M. Khoshgoftaar
2024	J	`jnl`	Blockchain meets machine learning: a survey. J. Big Data Safak Kayikci, Taghi M. Khoshgoftaar
2024	B	`conf`	Confident Labels: A Novel Approach to New Class Labeling and Evaluation on Highly Imbalanced Data. ICTAI Mary Anne Walauskis, Taghi M. Khoshgoftaar
2024	J	`jnl`	Data reduction techniques for highly imbalanced medicare Big Data. J. Big Data John T. Hancock, Huanjing Wang, Taghi M. Khoshgoftaar, Qianxin Liang
2024	B	`conf`	Enhancing Medicare Fraud Detection: Random Undersampling Followed by SHAP-Driven Feature Selection with Big Data. ICTAI Qianxin Liang, Richard A. Bauder, Taghi M. Khoshgoftaar
2024	J	`jnl`	Feature selection strategies: a comparative analysis of SHAP-value and importance-based methods. J. Big Data Huanjing Wang, Qianxin Liang, John T. Hancock, Taghi M. Khoshgoftaar
2024	J	`jnl`	Low-shot learning and class imbalance: a survey. J. Big Data Preston Billion Polak, Joseph D. Prusa, Taghi M. Khoshgoftaar
2024	C	`conf`	New Class Labeling and Evaluation Methodology for Balanced and Highly Imbalanced Data. ICMLA Mary Anne Walauskis, Taghi M. Khoshgoftaar
2024	J	`jnl`	Synthesizing class labels for highly imbalanced credit card fraud detection data. J. Big Data Robert K. L. Kennedy, Flavio Villanustre, Taghi M. Khoshgoftaar, Zahra Salekshahrezaee
2023	—	`conf`	A Comparative Study of Model-Agnostic and Importance-Based Feature Selection Approaches. CogMI Huanjing Wang, Qianxin Liang, John T. Hancock, Taghi M. Khoshgoftaar
2023	B	`conf`	A Model-Agnostic Feature Selection Technique to Improve the Performance of One-Class Classifiers. ICTAI John T. Hancock, Richard A. Bauder, Taghi M. Khoshgoftaar
2023	—	`conf`	A Novel Approach to Synthesize Class Labels in Highly Imbalanced Large Data. CogMI Robert K. L. Kennedy, Taghi M. Khoshgoftaar
2023	J	`jnl`	An approach to application-layer DoS detection. J. Big Data Clifford Kemp, Chad Calvert, Taghi M. Khoshgoftaar, Joffrey L. Leevy
2023	Misc	`conf`	Assessing One-Class and Binary Classification Approaches for Identifying Medicare Fraud. IRI Joffrey L. Leevy, John T. Hancock, Taghi M. Khoshgoftaar
2023	J	`jnl`	Breast cancer prediction using gated attentive multimodal deep learning. J. Big Data Safak Kayikci, Taghi M. Khoshgoftaar
2023	J	`jnl`	Comparative analysis of binary and one-class classification techniques for credit card fraud data. J. Big Data Joffrey L. Leevy, John T. Hancock, Taghi M. Khoshgoftaar
2023	C	`conf`	Data Reduction to Improve the Performance of One-Class Classifiers on Highly Imbalanced Big Data. ICMLA John T. Hancock, Taghi M. Khoshgoftaar
2023	J	`jnl`	Data-Centric AI for Healthcare Fraud Detection. SN Comput. Sci. Justin M. Johnson, Taghi M. Khoshgoftaar
2023	Misc	`conf`	Enhancing Credit Card Fraud Detection Through a Novel Ensemble Feature Selection Technique. IRI Huanjing Wang, Qianxin Liang, John T. Hancock, Taghi M. Khoshgoftaar
2023	J	`jnl`	Evaluating classifier performance with highly imbalanced Big Data. J. Big Data John T. Hancock, Taghi M. Khoshgoftaar, Justin M. Johnson
2023	J	`jnl`	Explainable machine learning models for Medicare fraud detection. J. Big Data John T. Hancock, Richard A. Bauder, Huanjing Wang, Taghi M. Khoshgoftaar
2023	J	`jnl`	Exploring Maximum Tree Depth and Random Undersampling in Ensemble Trees to Optimize the Classification of Imbalanced Big Data. SN Comput. Sci. John T. Hancock, Taghi M. Khoshgoftaar
2023	—	`conf`	Improving Medicare Fraud Detection through Big Data Size Reduction Techniques. SOSE Huanjing Wang, John T. Hancock, Taghi M. Khoshgoftaar
2023	J	`jnl`	Investigating the effectiveness of one-class and binary classification for fraud detection. J. Big Data Joffrey L. Leevy, John T. Hancock, Taghi M. Khoshgoftaar, Azadeh Abdollah Zadeh
2023	J	`jnl`	Iterative cleaning and learning of big highly-imbalanced fraud data using unsupervised learning. J. Big Data Robert K. L. Kennedy, Zahra Salekshahrezaee, Flavio Villanustre, Taghi M. Khoshgoftaar
2023	J	`jnl`	Learning from Highly Imbalanced Big Data with Label Noise. Int. J. Artif. Intell. Tools Justin M. Johnson, Robert K. L. Kennedy, Taghi M. Khoshgoftaar
2023	B	`conf`	One-Class Classifier Performance: Comparing Majority versus Minority Class Training. ICTAI Joffrey L. Leevy, John T. Hancock, Taghi M. Khoshgoftaar, Azadeh Abdollah Zadeh
2023	J	`jnl`	The effect of feature extraction and data sampling on credit card fraud detection. J. Big Data Zahra Salekshahrezaee, Joffrey L. Leevy, Taghi M. Khoshgoftaar
2023	J	`jnl`	Threshold optimization and random undersampling for imbalanced credit card data. J. Big Data Joffrey L. Leevy, Justin M. Johnson, John T. Hancock, Taghi M. Khoshgoftaar
2023	Misc	`conf`	Unsupervised Anomaly Detection of Class Imbalanced Cognition Data Using an Iterative Cleaning Method. IRI Robert K. L. Kennedy, Zahra Salekshahrezaee, Taghi M. Khoshgoftaar
2023	J	`jnl`	Using machine learning to identify patient characteristics to predict mortality of in-patients with COVID-19 in South Florida. Frontiers Digit. Health Debarshi Datta, Safiya George Dalmida, Laurie Martinez, David Newman, Javad Hashemi, Taghi M. Khoshgoftaar, Connor Shorten, Candice Sareli, Paula Eckardt
2022	Misc	`conf`	A Class-Imbalanced Study with Feature Extraction via PCA and Convolutional Autoencoder. IRI Zahra Salekshahrezaee, Joffrey L. Leevy, Taghi M. Khoshgoftaar
2022	—	`conf`	A Comparative Approach to Threshold Optimization for Classifying Imbalanced Data. CIC John T. Hancock, Justin M. Johnson, Taghi M. Khoshgoftaar
2022	Misc	`conf`	A Comparison of House Price Classification with Structured and Unstructured Text Data. FLAIRS Erika Cardenas, Connor Shorten, Taghi M. Khoshgoftaar, Borivoje Furht
2022	—	`conf`	A Novel Approach for Unsupervised Learning of Highly-Imbalanced Data. CogMI Robert K. L. Kennedy, Zahra Salekshahrezaee, Taghi M. Khoshgoftaar
2022	J	`jnl`	A Survey on Classifying Big Data with Label Noise. ACM J. Data Inf. Qual. Justin M. Johnson, Taghi M. Khoshgoftaar
2022	J	`jnl`	A new feature popularity framework for detecting cyberattacks using popular features. J. Big Data Richard Zuech, John T. Hancock, Taghi M. Khoshgoftaar
2022	Misc	`conf`	An Exploration of Consistency Learning with Data Augmentation. FLAIRS Connor Shorten, Taghi M. Khoshgoftaar
2022	C	`conf`	Cost-Sensitive Ensemble Learning for Highly Imbalanced Classification. ICMLA Justin M. Johnson, Taghi M. Khoshgoftaar
2022	J	`jnl`	Encoding High-Dimensional Procedure Codes for Healthcare Fraud Detection. SN Comput. Sci. Justin M. Johnson, Taghi M. Khoshgoftaar
2022	B	`conf`	Evaluating Performance Metrics for Credit Card Fraud Classification. ICTAI Joffrey L. Leevy, Taghi M. Khoshgoftaar, John T. Hancock
2022	B	`conf`	Exploring Language-Interfaced Fine-Tuning for COVID-19 Patient Survival Classification. ICTAI Connor Shorten, Erika Cardenas, Taghi M. Khoshgoftaar, Javad Hashemi, Safiya George Dalmida, David Newman, Debarshi Datta, Laurie Martinez, Candice Sareli, Paula Eckard
2022	B	`conf`	GANs for Class-Imbalanced Data: A Meta-Analysis of GitHub Projects. ICTAI Rick Sauber-Cole, Taghi M. Khoshgoftaar, Justin M. Johnson
2022	Misc	`conf`	Healthcare Provider Summary Data for Fraud Classification. IRI Justin M. Johnson, Taghi M. Khoshgoftaar
2022	J	`jnl`	Hyperparameter Tuning for Medicare Fraud Detection in Big Data. SN Comput. Sci. John T. Hancock, Taghi M. Khoshgoftaar
2022	C	`conf`	Informative Evaluation Metrics for Highly Imbalanced Big Data Classification. ICMLA John T. Hancock, Taghi M. Khoshgoftaar, Justin M. Johnson
2022	J	`jnl`	IoT information theft prediction using ensemble feature selection. J. Big Data Joffrey L. Leevy, John T. Hancock, Taghi M. Khoshgoftaar, Jared M. Peterson
2022	Misc	`conf`	Optimizing Ensemble Trees for Big Data Healthcare Fraud Detection. IRI John T. Hancock, Taghi M. Khoshgoftaar
2022	Misc	`conf`	Predicting the Severity of COVID-19 Respiratory Illness with Deep Learning. FLAIRS Connor Shorten, Taghi M. Khoshgoftaar, Javad Hashemi, Safiya George Dalmida, David Newman, Debarshi Datta, Laurie Martinez, Candice Sareli, Paula Eckard
2022	—	`conf`	The Effects of Random Undersampling for Big Data Medicare Fraud Detection. SOSE John T. Hancock, Taghi M. Khoshgoftaar, Justin M. Johnson
2022	J	`jnl`	The use of generative adversarial networks to alleviate class imbalance in tabular data: a survey. J. Big Data Rick Sauber-Cole, Taghi M. Khoshgoftaar
2021	—	`conf`	A Review and Analysis of the Bot-IoT Dataset. SOSE Jared M. Peterson, Joffrey L. Leevy, Taghi M. Khoshgoftaar
2021	J	`jnl`	A literature review on one-class classification and its potential applications in big data. J. Big Data Naeem Seliya, Azadeh Abdollah Zadeh, Taghi M. Khoshgoftaar
2021	J	`jnl`	A reconstruction error-based framework for label noise detection. J. Big Data Zahra Salekshahrezaee, Joffrey L. Leevy, Taghi M. Khoshgoftaar
2021	—	`conf`	An Easy-to-Classify Approach for the Bot-IoT Dataset. CogMI Joffrey L. Leevy, John T. Hancock, Taghi M. Khoshgoftaar, Jared M. Peterson
2021	—	`conf`	An Examination of Neural Networks on Cluster Computers. BigDataService Robert K. L. Kennedy, Taghi M. Khoshgoftaar
2021	J	`jnl`	Deep Learning applications for COVID-19. J. Big Data Connor Shorten, Taghi M. Khoshgoftaar, Borko Furht
2021	C	`conf`	Detecting Information Theft Attacks in the Bot-IoT Dataset. ICMLA Joffrey L. Leevy, John T. Hancock, Taghi M. Khoshgoftaar, Jared M. Peterson
2021	Misc	`conf`	Detecting SQL Injection Web Attacks Using Ensemble Learners and Data Sampling. CSR Richard Zuech, John T. Hancock, Taghi M. Khoshgoftaar
2021	C	`conf`	Detecting SSH and FTP Brute Force Attacks in Big Data. ICMLA John T. Hancock, Taghi M. Khoshgoftaar, Joffrey L. Leevy
2021	Misc	`conf`	Detecting Slow Application-Layer DoS Attacks With PCA. IRI Clifford Kemp, Chad Calvert, Taghi M. Khoshgoftaar
2021	Misc	`conf`	Detecting Web Attacks in Severely Imbalanced Network Traffic Data. IRI Richard Zuech, John T. Hancock, Taghi M. Khoshgoftaar
2021	J	`jnl`	Detecting cybersecurity attacks across different network features and learners. J. Big Data Joffrey L. Leevy, John T. Hancock, Richard Zuech, Taghi M. Khoshgoftaar
2021	J	`jnl`	Detecting web attacks using random undersampling and ensemble learners. J. Big Data Richard Zuech, John T. Hancock, Taghi M. Khoshgoftaar
2021	Misc	`conf`	Encoding Techniques for High-Cardinality Features and Ensemble Learners. IRI Justin M. Johnson, Taghi M. Khoshgoftaar
2021	B	`conf`	Feature Extraction for Class Imbalance Using a Convolutional Autoencoder and Data Sampling. ICTAI Zahra Salekshahrezaee, Joffrey L. Leevy, Taghi M. Khoshgoftaar
2021	C	`conf`	Feature Popularity Between Different Web Attacks with Supervised Feature Selection Rankers. ICMLA Richard Zuech, John T. Hancock, Taghi M. Khoshgoftaar
2021	J	`jnl`	Gradient Boosted Decision Tree Algorithms for Medicare Fraud Detection. SN Comput. Sci. John T. Hancock, Taghi M. Khoshgoftaar
2021	Misc	`conf`	Impact of Hyperparameter Tuning in Classifying Highly Imbalanced Big Data. IRI John T. Hancock, Taghi M. Khoshgoftaar
2021	B	`conf`	Investigating the Generalization of Image Classifiers with Augmented Test Sets. ICTAI Connor Shorten, Taghi M. Khoshgoftaar
2021	—	`conf`	IoT Reconnaissance Attack Classification with Random Undersampling and Ensemble Feature Selection. CIC Joffrey L. Leevy, John T. Hancock, Taghi M. Khoshgoftaar, Naeem Seliya
2021	C	`conf`	KerasBERT: Modeling the Keras Language. ICMLA Connor Shorten, Taghi M. Khoshgoftaar
2021	—	`conf`	Leveraging LightGBM for Categorical Big Data. BigDataService John T. Hancock, Taghi M. Khoshgoftaar
2021	J	`jnl`	Medical Provider Embeddings for Healthcare Fraud Detection. SN Comput. Sci. Justin M. Johnson, Taghi M. Khoshgoftaar
2021	—	`conf`	Mitigating Class Imbalance for IoT Network Intrusion Detection: A Survey. BigDataService Joffrey L. Leevy, Taghi M. Khoshgoftaar, Jared M. Peterson
2021	J	`jnl`	Modeling and tracking Covid-19 cases using Big Data analytics on HPCC system platformm. J. Big Data Flavio Villanustre, Arjuna Chala, Roger Dev, Lili Xu, Jesse Shaw, Borko Furht, Taghi M. Khoshgoftaar
2021	B	`conf`	Output Thresholding for Ensemble Learners and Imbalanced Big Data. ICTAI Justin M. Johnson, Taghi M. Khoshgoftaar
2021	—	`conf`	Predicting Traffic Incidents in Road Networks Using Vehicle Detector Data. ITSC Aline Anacleto, Taghi M. Khoshgoftaar, Evangelos I. Kaisar
2021	C	`conf`	Robust Thresholding Strategies for Highly Imbalanced and Noisy Data. ICMLA Justin M. Johnson, Taghi M. Khoshgoftaar
2021	J	`jnl`	Text Data Augmentation for Deep Learning. J. Big Data Connor Shorten, Taghi M. Khoshgoftaar, Borko Furht
2021	B	`conf`	The Effects of Class Label Noise on Highly-Imbalanced Big Data. ICTAI Robert K. L. Kennedy, Justin M. Johnson, Taghi M. Khoshgoftaar
2021	Misc	`conf`	Using Inductive Transfer Learning to Improve Hotel Review Spam Detection. IRI Michael Crawford, Taghi M. Khoshgoftaar
2020	—	`conf`	A Short Survey of LSTM Models for De-identification of Medical Free Text. CIC Joffrey L. Leevy, Taghi M. Khoshgoftaar
2020	J	`jnl`	A study on rare fraud predictions with big Medicare claims fraud data. Intell. Data Anal. Richard A. Bauder, Taghi M. Khoshgoftaar
2020	J	`jnl`	A survey and analysis of intrusion detection models based on CSE-CIC-IDS2018 Big Data. J. Big Data Joffrey L. Leevy, Taghi M. Khoshgoftaar
2020	C	`conf`	Accelerated Deep Learning on HPCC Systems. ICMLA Robert K. L. Kennedy, Taghi M. Khoshgoftaar
2020	J	`jnl`	CatBoost for big data: an interdisciplinary review. J. Big Data John T. Hancock, Taghi M. Khoshgoftaar
2020	—	`conf`	Detecting Cybersecurity Attacks Using Different Network Features with LightGBM and XGBoost Learners. CogMI Joffrey L. Leevy, John T. Hancock, Richard Zuech, Taghi M. Khoshgoftaar
2020	Misc	`conf`	Detection Methods of Slow Read DoS Using Full Packet Capture Data. IRI Clifford Kemp, Chad Calvert, Taghi M. Khoshgoftaar
2020	C	`conf`	Evaluating The Number of Trainable Parameters on Deep Maxout and LReLU Networks for Visual Recognition. ICMLA Gabriel Castaneda, Paul Morris, Taghi M. Khoshgoftaar
2020	—	`conf`	Hcpcs2Vec: Healthcare Procedure Embeddings for Medicare Fraud Prediction. CIC Justin M. Johnson, Taghi M. Khoshgoftaar
2020	J	`jnl`	Investigating class rarity in big data. J. Big Data Tawfiq Hasanin, Taghi M. Khoshgoftaar, Joffrey L. Leevy, Richard A. Bauder
2020	J	`jnl`	Investigating the relationship between time and predictive model maintenance. J. Big Data Joffrey L. Leevy, Taghi M. Khoshgoftaar, Richard A. Bauder, Naeem Seliya
2020	Misc	`conf`	Medicare Fraud Detection using CatBoost. IRI John T. Hancock, Taghi M. Khoshgoftaar
2020	C	`conf`	Performance of CatBoost and XGBoost in Medicare Fraud Detection. ICMLA John T. Hancock, Taghi M. Khoshgoftaar
2020	J	`jnl`	Sample size determination for biomedical big data with limited labels. Netw. Model. Anal. Health Informatics Bioinform. Aaron N. Richter, Taghi M. Khoshgoftaar
2020	Misc	`conf`	Semantic Embeddings for Medical Providers and Fraud Detection. IRI Justin M. Johnson, Taghi M. Khoshgoftaar
2020	J	`jnl`	Survey on RNN and CRF models for de-identification of medical free text. J. Big Data Joffrey L. Leevy, Taghi M. Khoshgoftaar, Flavio Villanustre
2020	J	`jnl`	Survey on categorical data for neural networks. J. Big Data John T. Hancock, Taghi M. Khoshgoftaar
2020	J	`jnl`	The Effects of Data Sampling with Deep Learning and Highly Imbalanced Big Data. Inf. Syst. Frontiers Justin M. Johnson, Taghi M. Khoshgoftaar
2019	C	`ed.`	18th IEEE International Conference On Machine Learning And Applications, ICMLA 2019, Boca Raton, FL, USA, December 16-19, 2019 ICMLA M. Arif Wani, Taghi M. Khoshgoftaar, Dingding Wang, Huanjing Wang, Naeem Seliya
2019	Misc	`conf`	A Comparison of Performance Metrics with Severely Imbalanced Network Security Big Data. IRI Tawfiq Hasanin, Taghi M. Khoshgoftaar, Joffrey L. Leevy
2019	C	`conf`	A Study on Software Metric Selection for Software Fault Prediction. ICMLA Huanjing Wang, Taghi M. Khoshgoftaar
2019	J	`jnl`	A parallel and distributed stochastic gradient descent implementation using commodity clusters. J. Big Data Robert K. L. Kennedy, Taghi M. Khoshgoftaar, Flavio Villanustre, Timothy Humphrey
2019	J	`jnl`	A survey on Image Data Augmentation for Deep Learning. J. Big Data Connor Shorten, Taghi M. Khoshgoftaar
2019	B	`conf`	Approximating Learning Curves for Imbalanced Big Data with Limited Labels. ICTAI Aaron N. Richter, Taghi M. Khoshgoftaar
2019	Misc	`conf`	Deep Learning and Data Sampling with Imbalanced Big Data. IRI Justin M. Johnson, Taghi M. Khoshgoftaar
2019	C	`conf`	Deep Learning and Thresholding with Class-Imbalanced Big Data. ICMLA Justin M. Johnson, Taghi M. Khoshgoftaar
2019	Misc	`conf`	Deep Learning with Maxout Activations for Visual Recognition and Verification. IRI Gabriel Castaneda, Paul Morris, Taghi M. Khoshgoftaar
2019	Misc	`conf`	Detecting Slow HTTP POST DoS Attacks Using Netflow Features. FLAIRS Chad Calvert, Clifford Kemp, Taghi M. Khoshgoftaar, Maryam M. Najafabadi
2019	—	`conf`	Differentiating between Educational Data Mining and Learning Analytics: A Bibliometric Approach. EDM (Workshops) Stevens Dormezil, Taghi M. Khoshgoftaar, Federica Robinson-Bryant
2019	J	`jnl`	Efficient learning from big data for cancer risk modeling: A case study with melanoma. Comput. Biol. Medicine Aaron N. Richter, Taghi M. Khoshgoftaar
2019	Misc	`conf`	Evaluating Model Predictive Performance: A Medicare Fraud Detection Case Study. IRI Richard A. Bauder, Matthew Herland, Taghi M. Khoshgoftaar
2019	J	`jnl`	Evaluation of maxout activations in deep learning across several big data domains. J. Big Data Gabriel Castaneda, Paul Morris, Taghi M. Khoshgoftaar
2019	J	`jnl`	Examining characteristics of predictive models with imbalanced big data. J. Big Data Tawfiq Hasanin, Taghi M. Khoshgoftaar, Joffrey L. Leevy, Naeem Seliya
2019	J	`jnl`	Extracting Knowledge from Technical Reports for the Valuation of West Texas Intermediate Crude Oil Futures. Inf. Syst. Frontiers Joseph D. Prusa, Ryan Sagul, Taghi M. Khoshgoftaar
2019	J	`jnl`	Impact of class distribution on the detection of slow HTTP DoS attacks using Big Data. J. Big Data Chad Calvert, Taghi M. Khoshgoftaar
2019	—	`conf`	Investigating Random Undersampling and Feature Selection on Bioinformatics Big Data. BigDataService Tawfiq Hasanin, Taghi M. Khoshgoftaar, Joffrey L. Leevy, Naeem Seliya
2019	Misc	`conf`	Investigation of Maxout Activations on Convolutional Neural Networks for Big Data Text Sentiment Analysis. FLAIRS Gabriel Castaneda, Paul Morris, Joseph D. Prusa, Taghi M. Khoshgoftaar
2019	C	`conf`	Learning Curve Estimation with Large Imbalanced Datasets. ICMLA Aaron N. Richter, Taghi M. Khoshgoftaar
2019	J	`jnl`	Maxout Networks for Visual Recognition. Int. J. Multim. Data Eng. Manag. Gabriel Castaneda, Paul Morris, Taghi M. Khoshgoftaar
2019	—	`conf`	Maxout Neural Network for Big Data Medical Fraud Detection. BigDataService Gabriel Castaneda, Paul Morris, Taghi M. Khoshgoftaar
2019	J	`jnl`	Medicare fraud detection using neural networks. J. Big Data Justin M. Johnson, Taghi M. Khoshgoftaar
2019	J	`jnl`	Melanoma risk modeling from limited positive samples. Netw. Model. Anal. Health Informatics Bioinform. Aaron N. Richter, Taghi M. Khoshgoftaar
2019	J	`jnl`	Random forest implementation and optimization for Big Data analytics on LexisNexis's high performance computing cluster platform. J. Big Data Victor M. Herrera, Taghi M. Khoshgoftaar, Flavio Villanustre, Borko Furht
2019	J	`jnl`	Severely imbalanced Big Data challenges: investigating data sampling approaches. J. Big Data Tawfiq Hasanin, Taghi M. Khoshgoftaar, Joffrey L. Leevy, Richard A. Bauder
2019	J	`jnl`	Survey on deep learning with class imbalance. J. Big Data Justin M. Johnson, Taghi M. Khoshgoftaar
2019	C	`conf`	The Effect of Time on the Maintenance of a Predictive Model. ICMLA Joffrey L. Leevy, Taghi M. Khoshgoftaar, Richard A. Bauder, Naeem Seliya
2019	J	`jnl`	The effects of class rarity on the evaluation of supervised healthcare fraud detection models. J. Big Data Matthew Herland, Richard A. Bauder, Taghi M. Khoshgoftaar
2019	B	`conf`	Threshold Based Optimization of Performance Metrics with Severely Imbalanced Big Security Data. ICTAI Chad Calvert, Taghi M. Khoshgoftaar
2018	J	`jnl`	A Study of the Impact of Base Traditional Learners on Transfer Learning Algorithms. Int. J. Artif. Intell. Tools Karl R. Weiss, Taghi M. Khoshgoftaar
2018	Misc	`conf`	A Survey of Medicare Data Processing and Integration for Fraud Detection. IRI Richard A. Bauder, Taghi M. Khoshgoftaar
2018	J	`jnl`	A review of statistical and machine learning methods for modeling cancer risk using structured clinical data. Artif. Intell. Medicine Aaron N. Richter, Taghi M. Khoshgoftaar
2018	J	`jnl`	A survey on addressing high-class imbalance in big data. J. Big Data Joffrey L. Leevy, Taghi M. Khoshgoftaar, Richard A. Bauder, Naeem Seliya
2018	C	`conf`	An Empirical Study on Class Rarity in Big Data. ICMLA Richard A. Bauder, Taghi M. Khoshgoftaar, Tawfiq Hasanin
2018	J	`jnl`	Big Data fraud detection using multiple medicare data sources. J. Big Data Matthew Herland, Taghi M. Khoshgoftaar, Richard A. Bauder
2018	J	`jnl`	Big Data: Deep Learning for financial sentiment analysis. J. Big Data Sahar Sohangir, Dingding Wang, Anna Pomeranets, Taghi M. Khoshgoftaar
2018	B	`conf`	Building and Interpreting Risk Models from Imbalanced Clinical Data. ICTAI Aaron N. Richter, Taghi M. Khoshgoftaar
2018	B	`conf`	Data Sampling Approaches with Severely Imbalanced Big Data for Medicare Fraud Detection. ICTAI Richard A. Bauder, Taghi M. Khoshgoftaar, Tawfiq Hasanin
2018	Misc	`conf`	Filter-Based Subset Selection for Easy, Moderate, and Hard Bioinformatics Data. IRI Ahmad Abu Shanab, Taghi M. Khoshgoftaar
2018	Misc	`conf`	Fraud Detection with a Limited Number of Known Fraudulent Medicare Providers. FLAIRS Richard A. Bauder, Taghi M. Khoshgoftaar, Amri Napolitano
2018	Misc	`conf`	Identifying Medicare Provider Fraud with Unsupervised Machine Learning. IRI Richard A. Bauder, Raquel da Rosa, Taghi M. Khoshgoftaar
2018	Misc	`conf`	Is Gene Selection Enough for Imbalanced Bioinformatics Data? IRI Ahmad Abu Shanab, Taghi M. Khoshgoftaar
2018	Misc	`conf`	Location-Based Twitter Sentiment Analysis for Predicting the U.S. 2016 Presidential Election. FLAIRS Brian Heredia, Joseph D. Prusa, Taghi M. Khoshgoftaar
2018	Misc	`conf`	Medicare Fraud Detection Using Random Forest with Class Imbalanced Big Data. IRI Richard A. Bauder, Taghi M. Khoshgoftaar
2018	—	`conf`	Melanoma Risk Prediction with Structured Electronic Health Records. BCB Aaron N. Richter, Taghi M. Khoshgoftaar
2018	J	`jnl`	Social media for polling and predicting United States election outcome. Soc. Netw. Anal. Min. Brian Heredia, Joseph D. Prusa, Taghi M. Khoshgoftaar
2018	Misc	`conf`	The Detection of Medicare Fraud Using Machine Learning Methods with Excluded Provider Labels. FLAIRS Richard A. Bauder, Taghi M. Khoshgoftaar
2018	Misc	`conf`	The Effects of Random Undersampling with Simulated Class Imbalance for Big Data. IRI Tawfiq Hasanin, Taghi M. Khoshgoftaar
2018	—	`conf`	The Impact of Malicious Accounts on Political Tweet Sentiment. CIC Brian Heredia, Joseph D. Prusa, Taghi M. Khoshgoftaar
2018	J	`jnl`	The effects of varying class distribution on learner behavior for medicare fraud detection with imbalanced big data. Health Inf. Sci. Syst. Richard A. Bauder, Taghi M. Khoshgoftaar
2018	Misc	`conf`	Utilizing Netflow Data to Detect Slow Read Attacks. IRI Clifford Kemp, Chad Calvert, Taghi M. Khoshgoftaar
2017	—	`conf`	A Review of Performance Evaluation on 2D Face Databases. BigDataService Gabriel Castaneda, Taghi M. Khoshgoftaar
2017	Misc	`conf`	A Text Mining Approach for Anomaly Detection in Application Layer DDoS Attacks. FLAIRS Maryam M. Najafabadi, Taghi M. Khoshgoftaar, Chad Calvert, Clifford Kemp
2017	J	`jnl`	A survey on heterogeneous transfer learning. J. Big Data Oscar Day, Taghi M. Khoshgoftaar
2017	Misc	`conf`	Analysis of Transfer Learning Performance Measures. IRI Karl R. Weiss, Taghi M. Khoshgoftaar
2017	C	`conf`	Comparing Transfer Learning and Traditional Learning Under Domain Class Imbalance. ICMLA Karl R. Weiss, Taghi M. Khoshgoftaar
2017	Misc	`conf`	Deep Neural Network Architecture for Character-Level Learning on Short Text. FLAIRS Joseph D. Prusa, Taghi M. Khoshgoftaar
2017	—	`conf`	Detection of Phishing Webpages Using Heterogeneous Transfer Learning. CIC Karl R. Weiss, Taghi M. Khoshgoftaar
2017	Misc	`conf`	Estimating Outlier Score Probabilities. IRI Richard A. Bauder, Taghi M. Khoshgoftaar
2017	B	`conf`	Evaluation of Transfer Learning Algorithms Using Different Base Learners. ICTAI Karl R. Weiss, Taghi M. Khoshgoftaar
2017	—	`conf`	Exploring the Effectiveness of Twitter at Polling the United States 2016 Presidential Election. CIC Brian Heredia, Joseph D. Prusa, Taghi M. Khoshgoftaar
2017	Misc	`conf`	Extracting Knowledge from Technical Reports for the Valuation of West Texas Intermediate Crude Oil Futures. IRI Joseph D. Prusa, Ryan Sagul, Taghi M. Khoshgoftaar, Michael Sterling
2017	J	`jnl`	Improving deep neural network design with new text data representations. J. Big Data Joseph D. Prusa, Taghi M. Khoshgoftaar
2017	J	`jnl`	Improving detection of untrustworthy online reviews using ensemble learners combined with feature selection. Soc. Netw. Anal. Min. Brian Heredia, Taghi M. Khoshgoftaar, Joseph D. Prusa, Michael Crawford
2017	J	`jnl`	Large-scale distributed L-BFGS. J. Big Data Maryam M. Najafabadi, Taghi M. Khoshgoftaar, Flavio Villanustre, John Holt
2017	Misc	`conf`	Medical Provider Specialty Predictions for the Detection of Anomalous Medicare Insurance Claims. IRI Matthew Herland, Richard A. Bauder, Taghi M. Khoshgoftaar
2017	C	`conf`	Medicare Fraud Detection Using Machine Learning Methods. ICMLA Richard A. Bauder, Taghi M. Khoshgoftaar
2017	Misc	`conf`	Modernizing Analytics for Melanoma with a Large-Scale Research Dataset. IRI Aaron N. Richter, Taghi M. Khoshgoftaar
2017	Misc	`conf`	Multivariate Anomaly Detection in Medicare using Model Residuals and Probabilistic Programming. FLAIRS Richard A. Bauder, Taghi M. Khoshgoftaar
2017	—	`conf`	Predicting sentinel node status in melanoma from a real-world EHR dataset. BIBM Aaron N. Richter, Taghi M. Khoshgoftaar
2017	J	`jnl`	Reliability Evaluation Model of Component-Based Software Based on Complex Network Theory. Qual. Reliab. Eng. Int. Kewen Li, Lu Liu, Jiannan Zhai, Taghi M. Khoshgoftaar, Mingwen Shao, Wenying Liu
2017	B	`conf`	Training Convolutional Networks on Truncated Text. ICTAI Joseph D. Prusa, Taghi M. Khoshgoftaar
2017	Misc	`conf`	User Behavior Anomaly Detection for Application Layer DDoS Attacks. IRI Maryam M. Najafabadi, Taghi M. Khoshgoftaar, Chad Calvert, Clifford Kemp
2017	Misc	`conf`	Using Weather and Playing Surface to Predict the Occurrence of Injury in Major League Soccer Games: A Case Study. IRI Sara Landset, Michael F. Bergeron, Taghi M. Khoshgoftaar
2016	Misc	`conf`	A Novel Method for Fraudulent Medicare Claims Detection from Expected Payment Deviations (Application Paper). IRI Richard A. Bauder, Taghi M. Khoshgoftaar
2016	C	`conf`	A Probabilistic Programming Approach for Outlier Detection in Healthcare Claims. ICMLA Richard A. Bauder, Taghi M. Khoshgoftaar
2016	J	`jnl`	A survey of transfer learning. J. Big Data Karl R. Weiss, Taghi M. Khoshgoftaar, Dingding Wang
2016	C	`conf`	An Investigation of Ensemble Techniques for Detection of Spam Reviews. ICMLA Brian Heredia, Taghi M. Khoshgoftaar, Joseph D. Prusa, Michael Crawford
2016	B	`conf`	An Investigation of Transfer Learning and Traditional Machine Learning Algorithms. ICTAI Karl R. Weiss, Taghi M. Khoshgoftaar
2016	Misc	`conf`	Comparing Approaches for Combining Data Sampling and Feature Selection to Address Key Data Quality Issues in Tweet Sentiment Analysis. FLAIRS Joseph D. Prusa, Taghi M. Khoshgoftaar
2016	Misc	`conf`	Cross-Domain Sentiment Analysis: An Empirical Investigation. IRI Brian Heredia, Taghi M. Khoshgoftaar, Joseph D. Prusa, Michael Crawford
2016	—	`ch.`	Deep Learning Techniques in Big Data Analytics. Big Data Technologies and Applications Maryam M. Najafabadi, Flavio Villanustre, Taghi M. Khoshgoftaar, Naeem Seliya, Randall Wald, Edin Muharemagic
2016	Misc	`conf`	Designing a Better Data Representation for Deep Neural Networks and Text Classification. IRI Joseph D. Prusa, Taghi M. Khoshgoftaar
2016	Misc	`conf`	Designing a Testing Framework for Transfer Learning Algorithms (Application Paper). IRI Karl R. Weiss, Taghi M. Khoshgoftaar, Oneeb Rehman
2016	Misc	`conf`	Enhancing Ensemble Learners with Data Sampling on High-Dimensional Imbalanced Tweet Sentiment Data. FLAIRS Joseph D. Prusa, Taghi M. Khoshgoftaar, Naeem Seliya
2016	—	`conf`	Integrating Multiple Data Sources to Enhance Sentiment Prediction. CIC Brian Heredia, Taghi M. Khoshgoftaar, Joseph D. Prusa, Michael Crawford
2016	C	`conf`	Investigating Transfer Learners for Robustness to Domain Class Imbalance. ICMLA Karl R. Weiss, Taghi M. Khoshgoftaar
2016	Misc	`conf`	Investigating the Variation of Ensemble Size on Bagging-Based Classifier Performance in Imbalanced Bioinformatics Datasets. IRI Alireza Fazelpour, Taghi M. Khoshgoftaar, David J. Dittman, Amri Napolitano
2016	—	`ch.`	Is Data Sampling Required When Using Random Forest for Classification on Imbalanced Bioinformatics Data? Theoretical Information Reuse and Integration David J. Dittman, Taghi M. Khoshgoftaar, Amri Napolitano
2016	Misc	`conf`	Necessity of Feature Selection when Augmenting Tweet Sentiment Feature Spaces with Emoticons. FLAIRS Joseph D. Prusa, Taghi M. Khoshgoftaar, Amri Napolitano
2016	Misc	`conf`	Predicting Cancer Relapse with Clinical Data: A Survey of Current Techniques. IRI Aaron N. Richter, Taghi M. Khoshgoftaar
2016	B	`conf`	Predicting Medical Provider Specialties to Detect Anomalous Insurance Claims. ICTAI Richard A. Bauder, Taghi M. Khoshgoftaar, Aaron N. Richter, Matthew Herland
2016	Misc	`conf`	RUDY Attack: Detection at the Network Level and Its Important Features. FLAIRS Maryam M. Najafabadi, Taghi M. Khoshgoftaar, Amri Napolitano, Charles Wheelus
2016	Misc	`conf`	Reducing Feature Set Explosion to Facilitate Real-World Review Spam Detection. FLAIRS Michael Crawford, Taghi M. Khoshgoftaar, Joseph D. Prusa
2016	J	`jnl`	The improved grey model based on particle swarm optimization algorithm for time series prediction. Eng. Appl. Artif. Intell. Kewen Li, Lu Liu, Jiannan Zhai, Taghi M. Khoshgoftaar, Timing Li
2016	—	`ch.`	Transfer Learning Techniques. Big Data Technologies and Applications Karl R. Weiss, Taghi M. Khoshgoftaar, Dingding Wang
2015	Misc	`conf`	A Multi-dimensional Comparison of Toolkits for Machine Learning with Big Data. IRI Aaron N. Richter, Taghi M. Khoshgoftaar, Sara Landset, Tawfiq Hasanin
2015	Misc	`conf`	A New Intrusion Detection Benchmarking System. FLAIRS Richard Zuech, Taghi M. Khoshgoftaar, Naeem Seliya, Maryam M. Najafabadi, Clifford Kemp
2015	Misc	`conf`	A Survey of 2D Face Databases. IRI Gabriel Castaneda, Taghi M. Khoshgoftaar
2015	J	`jnl`	A survey of open source tools for machine learning with big data in the Hadoop ecosystem. J. Big Data Sara Landset, Taghi M. Khoshgoftaar, Aaron N. Richter, Tawfiq Hasanin
2015	J	`jnl`	Aggregating Data Sampling with Feature Subset Selection to Address Skewed Software Defect Data. Int. J. Softw. Eng. Knowl. Eng. Kehan Gao, Taghi M. Khoshgoftaar, Amri Napolitano
2015	Misc	`conf`	Alterations to the Bootstrapping Process within Random Forest: A Case Study on Imbalanced Bioinformatics Data. IRI Taghi M. Khoshgoftaar, Alireza Fazelpour, David J. Dittman, Amri Napolitano
2015	J	`jnl`	An Empirical Investigation on Wrapper-Based Feature Selection for Predicting Software Quality. Int. J. Softw. Eng. Knowl. Eng. Huanjing Wang, Taghi M. Khoshgoftaar, Amri Napolitano
2015	Misc	`conf`	Building an Effective Classification Model for Breast Cancer Patient Response Data. IRI Brian Heredia, Taghi M. Khoshgoftaar, Alireza Fazelpour, David J. Dittman
2015	Misc	`conf`	Choosing an Appropriate Ensemble Classifier for Balanced Bioinformatics Data. IRI Alireza Fazelpour, Taghi M. Khoshgoftaar, David J. Dittman, Amri Napolitano
2015	C	`conf`	Combining Feature Subset Selection and Data Sampling for Coping with Highly Imbalanced Software Data. SEKE Kehan Gao, Taghi M. Khoshgoftaar, Amri Napolitano
2015	J	`jnl`	Deep learning applications and challenges in big data analytics. J. Big Data Maryam M. Najafabadi, Flavio Villanustre, Taghi M. Khoshgoftaar, Naeem Seliya, Randall Wald, Edin Muharemagic
2015	C	`conf`	Detection of SSH Brute Force Attacks Using Aggregated Netflow Data. ICMLA Maryam M. Najafabadi, Taghi M. Khoshgoftaar, Chad Calvert, Clifford Kemp
2015	C	`conf`	Does the Inclusion of Data Sampling Improve the Performance of Boosting Algorithms on Imbalanced Bioinformatics Data? ICMLA Alireza Fazelpour, Taghi M. Khoshgoftaar, David J. Dittman, Amri Napolitano
2015	B	`conf`	Efficient Modeling of User-Entity Preference in Big Social Networks. ICTAI Aaron N. Richter, Michael Crawford, Brian Heredia, Taghi M. Khoshgoftaar
2015	B	`conf`	Ensemble vs. Data Sampling: Which Option Is Best Suited to Improve Classification Performance of Imbalanced Bioinformatics Data? ICTAI Taghi M. Khoshgoftaar, Alireza Fazelpour, David J. Dittman, Amri Napolitano
2015	Misc	`conf`	Impact of Feature Selection Techniques for Tweet Sentiment Classification. FLAIRS Joseph D. Prusa, Taghi M. Khoshgoftaar, David J. Dittman
2015	J	`jnl`	Intrusion detection and Big Heterogeneous Data: a Survey. J. Big Data Richard Zuech, Taghi M. Khoshgoftaar, Randall Wald
2015	C	`conf`	Investigating New Bootstrapping Approaches of Bagging Classifiers to Account for Class Imbalance in Bioinformatics Datasets. ICMLA Alireza Fazelpour, Taghi M. Khoshgoftaar, David J. Dittman, Amri Napolitano
2015	J	`jnl`	Investigating Two Approaches for Adding Feature Ranking to Sampled Ensemble Learning for Software Quality Estimation. Int. J. Softw. Eng. Knowl. Eng. Kehan Gao, Taghi M. Khoshgoftaar, Amri Napolitano
2015	Misc	`conf`	Observing the Effect of the Choice of Classifier on Bioinformatics Data with Varying Levels of Data Quality and Class Balance. IRI Alireza Fazelpour, Taghi M. Khoshgoftaar, David J. Dittman, Ahmad Abu Shanab
2015	J	`jnl`	On the Stability of Feature Selection Methods in Software Quality Prediction: An Empirical Investigation. Int. J. Softw. Eng. Knowl. Eng. Huanjing Wang, Taghi M. Khoshgoftaar, Naeem Seliya
2015	Misc	`conf`	Selecting the Appropriate Ensemble Learning Approach for Balanced Bioinformatics Data. FLAIRS David J. Dittman, Taghi M. Khoshgoftaar, Amri Napolitano
2015	C	`conf`	Stability of Three Forms of Feature Selection Methods on Software Engineering Data. SEKE Huanjing Wang, Taghi M. Khoshgoftaar, Amri Napolitano
2015	J	`jnl`	Survey of review spam detection using machine learning techniques. J. Big Data Michael Crawford, Taghi M. Khoshgoftaar, Joseph D. Prusa, Aaron N. Richter, Hamzah Al Najada
2015	Misc	`conf`	The Effect of Data Sampling When Using Random Forest on Imbalanced Bioinformatics Data. IRI David J. Dittman, Taghi M. Khoshgoftaar, Amri Napolitano
2015	C	`conf`	The Effect of Dataset Size on Training Tweet Sentiment Classifiers. ICMLA Joseph D. Prusa, Taghi M. Khoshgoftaar, Naeem Seliya
2015	Misc	`conf`	Using Ensemble Learners to Improve Classifier Performance on Tweet Sentiment Data. IRI Joseph D. Prusa, Taghi M. Khoshgoftaar, David J. Dittman
2015	B	`conf`	Using Feature Selection in Combination with Ensemble Learning Techniques to Improve Tweet Sentiment Classification Performance. ICTAI Joseph D. Prusa, Taghi M. Khoshgoftaar, Amri Napolitano
2015	Misc	`conf`	Using Random Undersampling to Alleviate Class Imbalance on Tweet Sentiment Data. IRI Joseph D. Prusa, Taghi M. Khoshgoftaar, David J. Dittman, Amri Napolitano
2015	C	`conf`	Utilizing Ensemble, Data Sampling and Feature Selection Techniques for Improving Classification Performance on Tweet Sentiment Data. ICMLA Joseph D. Prusa, Taghi M. Khoshgoftaar, Amri Napolitano
2014	C	`conf`	A Session Based Approach for Aggregating Network Traffic Data - The SANTA Dataset. BIBE Charles Wheelus, Taghi M. Khoshgoftaar, Richard Zuech, Maryam M. Najafabadi
2014	J	`jnl`	A comparative study of iterative and non-iterative feature selection techniques for software defect prediction. Inf. Syst. Frontiers Taghi M. Khoshgoftaar, Kehan Gao, Amri Napolitano, Randall Wald
2014	J	`jnl`	A review of data mining using big data in health informatics. J. Big Data Matthew Herland, Taghi M. Khoshgoftaar, Randall Wald
2014	J	`jnl`	An empirical study of the classification performance of learners on imbalanced and noisy software quality data. Inf. Sci. Chris Seiffert, Taghi M. Khoshgoftaar, Jason Van Hulse, Andres Folleco
2014	C	`conf`	Choosing the Best Classification Performance Metric for Wrapper-based Software Metric Selection for Defect Prediction. SEKE Huanjing Wang, Taghi M. Khoshgoftaar, Amri Napolitano
2014	Misc	`conf`	Classification performance of three approaches for combining data sampling and gene selection on bioinformatics data. IRI Taghi M. Khoshgoftaar, Alireza Fazelpour, David J. Dittman, Amri Napolitano
2014	Misc	`conf`	Combining Feature Selection and Ensemble Learning for Software Quality Estimation. FLAIRS Kehan Gao, Taghi M. Khoshgoftaar, Randall Wald
2014	C	`conf`	Comparing Two Approaches for Adding Feature Ranking to Sampled Ensemble Learning for Software Quality Estimation. SEKE Kehan Gao, Taghi M. Khoshgoftaar, Amri Napolitano
2014	Misc	`conf`	Comparison of Data Sampling Approaches for Imbalanced Bioinformatics Data. FLAIRS David J. Dittman, Taghi M. Khoshgoftaar, Randall Wald, Amri Napolitano
2014	C	`conf`	Effects of the Use of Boosting on Classification Performance of Imbalanced Bioinformatics Datasets. BIBE Taghi M. Khoshgoftaar, Alireza Fazelpour, David J. Dittman, Amri Napolitano
2014	C	`conf`	Evaluation of Wrapper-Based Feature Selection Using Hard, Moderate, and Easy Bioinformatics Data. BIBE Ahmad Abu Shanab, Taghi M. Khoshgoftaar, Randall Wald
2014	Misc	`conf`	How ranker and learner choice affects classification performance on noisy bioinformatics data. IRI Ahmad Abu Shanab, Taghi M. Khoshgoftaar, Randall Wald, Amri Napolitano
2014	Misc	`conf`	Improving software quality estimation by combining feature selection strategies with sampled ensemble learning. IRI Taghi M. Khoshgoftaar, Kehan Gao, Amri Napolitano
2014	J	`jnl`	Incomplete-case nearest neighbor imputation in software measurement data. Inf. Sci. Jason Van Hulse, Taghi M. Khoshgoftaar
2014	C	`conf`	Machine Learning for Detecting Brute Force Attacks at the Network Level. BIBE Maryam M. Najafabadi, Taghi M. Khoshgoftaar, Clifford Kemp, Naeem Seliya, Richard Zuech
2014	C	`conf`	Network Traffic Prediction Models for Near- and Long-Term Predictions. BIBE Randall Wald, Taghi M. Khoshgoftaar, Richard Zuech, Amri Napolitano
2014	Misc	`conf`	Optimizing Wrapper-Based Feature Selection for Use on Bioinformatics Data. FLAIRS Randall Wald, Taghi M. Khoshgoftaar, Amri Napolitano
2014	Misc	`conf`	Rotation invariant face recognition survey. IRI Gabriel Castaneda Oscos, Taghi M. Khoshgoftaar, Randall Wald
2014	C	`conf`	Select-Bagging: Effectively Combining Gene Selection and Bagging for Balanced Bioinformatics Data. BIBE David J. Dittman, Taghi M. Khoshgoftaar, Amri Napolitano, Alireza Fazelpour
2014	C	`conf`	Selecting the Appropriate Data Sampling Approach for Imbalanced and High-Dimensional Bioinformatics Datasets. BIBE David J. Dittman, Taghi M. Khoshgoftaar, Amri Napolitano
2014	J	`jnl`	Software quality assessment using a multi-strategy classifier. Inf. Sci. Taghi M. Khoshgoftaar, Yudong Xiao, Kehan Gao
2014	Misc	`conf`	Stability of filter- and wrapper-based software metric selection techniques. IRI Huanjing Wang, Taghi M. Khoshgoftaar, Amri Napolitano
2014	J	`jnl`	System regression test planning with a fuzzy expert system. Inf. Sci. Zhiwei Xu, Kehan Gao, Taghi M. Khoshgoftaar, Naeem Seliya
2014	J	`jnl`	The Use of Ensemble-Based Data Preprocessing Techniques for Software Defect Prediction. Int. J. Softw. Eng. Knowl. Eng. Kehan Gao, Taghi M. Khoshgoftaar, Amri Napolitano
2014	Misc	`conf`	The effect of noise level and distribution on classification of easy gene microarray data. IRI Randall Wald, Taghi M. Khoshgoftaar, Ahmad Abu Shanab
2014	C	`conf`	Using Correlation-Based Feature Selection for a Diverse Collection of Bioinformatics Datasets. BIBE Randall Wald, Taghi M. Khoshgoftaar, Amri Napolitano
2014	Misc	`conf`	Using feature selection and classification to build effective and efficient firewalls. IRI Randall Wald, Flavio Villanustre, Taghi M. Khoshgoftaar, Richard Zuech, Jarvis Robinson, Edin Muharemagic
2013	B	`conf`	A Review of Ensemble Classification for DNA Microarrays Data. ICTAI Taghi M. Khoshgoftaar, David J. Dittman, Randall Wald, Wael Awada
2013	J	`jnl`	A Study of Software Metric Selection Techniques: stability Analysis and Defect Prediction Model Performance. Int. J. Artif. Intell. Tools Huanjing Wang, Taghi M. Khoshgoftaar, Qianhui Althea Liang
2013	C	`conf`	A Study on First Order Statistics-Based Feature Selection Techniques on Software Metric Data. SEKE Huanjing Wang, Taghi M. Khoshgoftaar, Randall Wald, Amri Napolitano
2013	Misc	`conf`	A survey of stability analysis of feature subset selection techniques. IRI Taghi M. Khoshgoftaar, Alireza Fazelpour, Huanjing Wang, Randall Wald
2013	—	`conf`	An Empirical Study on Wrapper-Based Feature Selection for Software Engineering Data. ICMLA (2) Huanjing Wang, Taghi M. Khoshgoftaar, Amri Napolitano
2013	Misc	`conf`	Classification Performance of Rank Aggregation Techniques for Ensemble Gene Selection. FLAIRS David J. Dittman, Taghi M. Khoshgoftaar, Randall Wald, Amri Napolitano
2013	—	`conf`	Comparative Analysis on the Stability of Feature Selection Techniques Using Three Frameworks on Biological Datasets. ICMLA (1) Randall Wald, Taghi M. Khoshgoftaar, Ahmad Abu Shanab, Amri Napolitano
2013	—	`conf`	Comparison of Stability for Different Families of Filter-Based and Wrapper-Based Feature Selection. ICMLA (2) Randall Wald, Taghi M. Khoshgoftaar, Amri Napolitano
2013	B	`conf`	Comparison of Two Frameworks for Measuring the Stability of Gene-Selection Techniques on Noisy Class-Imbalanced Data. ICTAI Randall Wald, Taghi M. Khoshgoftaar, Ahmad Abu Shanab
2013	Misc	`conf`	Comparison of rank-based vs. score-based aggregation for ensemble gene selection. IRI David J. Dittman, Taghi M. Khoshgoftaar, Randall Wald, Amri Napolitano
2013	—	`conf`	Contrasting Undersampled Boosting with Internal and External Feature Selection for Patient Response Datasets. ICMLA (2) Taghi M. Khoshgoftaar, David J. Dittman, Randall Wald, Amri Napolitano
2013	J	`jnl`	Editorial. Int. J. Artif. Intell. Tools Xingquan Zhu, Taghi M. Khoshgoftaar
2013	Misc	`conf`	Ensemble Gene Selection Versus Single Gene Selection: Which Is Better? FLAIRS Randall Wald, Taghi M. Khoshgoftaar, David J. Dittman
2013	C	`conf`	Exploring Ensemble-Based Data Preprocessing Techniques for Software Quality Estimation. SEKE Kehan Gao, Taghi M. Khoshgoftaar, Amri Napolitano
2013	J	`jnl`	Feature Selection for Optimization of Wavelet Packet Decomposition in Reliability Analysis of Systems. Int. J. Artif. Intell. Tools Randall Wald, Taghi M. Khoshgoftaar, John C. Sloan
2013	Misc	`conf`	Feature list aggregation approaches for ensemble gene selection on patient response datasets. IRI Taghi M. Khoshgoftaar, Randall Wald, David J. Dittman, Amri Napolitano
2013	Misc	`conf`	Filter- and wrapper-based feature selection for predicting user interaction with Twitter bots. IRI Randall Wald, Taghi M. Khoshgoftaar, Amri Napolitano
2013	Misc	`conf`	Gene selection stability's dependence on dataset difficulty. IRI David J. Dittman, Taghi M. Khoshgoftaar, Randall Wald, Amri Napolitano
2013	Misc	`conf`	Hidden dependencies between class imbalance and difficulty of learning for bioinformatics datasets. IRI Randall Wald, Taghi M. Khoshgoftaar, Alireza Fazelpour, David J. Dittman
2013	J	`jnl`	High Consequence Systems and Semantic Computing. Int. J. Semantic Comput. Victor L. Winter, Bojan Cukic, Taghi M. Khoshgoftaar, Kinji Mori, Raymond A. Paul, Carlos Pérez Leguízamo, Sahra Sedigh Sarvestani, John C. Sloan, Mladen A. Vouk, I-Ling Yen
2013	B	`conf`	How the Choice of Wrapper Learner and Performance Metric Affects Subset Evaluation. ICTAI Randall Wald, Taghi M. Khoshgoftaar, Amri Napolitano
2013	—	`conf`	Improving Software Quality Estimation by Combining Boosting and Feature Selection. ICMLA (1) Kehan Gao, Taghi M. Khoshgoftaar, Amri Napolitano
2013	B	`conf`	Maximizing Classification Performance for Patient Response Datasets. ICTAI David J. Dittman, Taghi M. Khoshgoftaar, Randall Wald, Amri Napolitano
2013	C	`conf`	Overcoming Big Data Challenges. SEKE Taghi M. Khoshgoftaar
2013	Misc	`conf`	Patient response datasets: Challenges and opportunities. IRI Randall Wald, Taghi M. Khoshgoftaar
2013	Misc	`conf`	Predicting susceptibility to social bots on Twitter. IRI Randall Wald, Taghi M. Khoshgoftaar, Amri Napolitano, Chris Sumner
2013	—	`conf`	Random Forest with 200 Selected Features: An Optimal Model for Bioinformatics Research. ICMLA (1) Randall Wald, Taghi M. Khoshgoftaar, David J. Dittman, Amri Napolitano
2013	B	`conf`	Should the Same Learners Be Used Both within Wrapper Feature Selection and for Building Classification Models? ICTAI Randall Wald, Taghi M. Khoshgoftaar, Amri Napolitano
2013	—	`conf`	Simplifying the Utilization of Machine Learning Techniques for Bioinformatics. ICMLA (2) David J. Dittman, Taghi M. Khoshgoftaar, Randall Wald, Amri Napolitano
2013	B	`conf`	Stability of Filter- and Wrapper-Based Feature Subset Selection. ICTAI Randall Wald, Taghi M. Khoshgoftaar, Amri Napolitano
2013	—	`conf`	Survey of Clinical Data Mining Applications on Big Data in Health Informatics. ICMLA (2) Matthew Herland, Taghi M. Khoshgoftaar, Randall Wald
2013	—	`conf`	Survey of Data Cleansing and Monitoring for Large-Scale Battery Backup Installations. ICMLA (2) Liz Aranguren Pachano, Taghi M. Khoshgoftaar, Randall Wald
2013	Misc	`conf`	The importance of performance metrics within wrapper feature selection. IRI Randall Wald, Taghi M. Khoshgoftaar, Amri Napolitano
2013	Misc	`conf`	The use of balance-aware subsampling for bioinformatics datasets. IRI Randall Wald, Taghi M. Khoshgoftaar, Alireza Fazelpour
2013	B	`conf`	Which Users Reply to and Interact with Twitter Social Bots? ICTAI Randall Wald, Taghi M. Khoshgoftaar, Amri Napolitano, Chris Sumner
2012	—	`conf`	A Comparative Study on the Stability of Software Metric Selection Techniques. ICMLA (2) Huanjing Wang, Taghi M. Khoshgoftaar, Randall Wald, Amri Napolitano
2012	—	`conf`	A Hybrid Approach to Coping with High Dimensionality and Class Imbalance for Software Defect Prediction. ICMLA (2) Kehan Gao, Taghi M. Khoshgoftaar, Amri Napolitano
2012	—	`conf`	A New Fixed-Overlap Partitioning Algorithm for Determining Stability of Bioinformatics Gene Rankers. ICMLA (2) Randall Wald, Taghi M. Khoshgoftaar, David J. Dittman
2012	—	`conf`	A Novel Noise-Resistant Boosting Algorithm for Class-Skewed Data. ICMLA (2) Jason Van Hulse, Taghi M. Khoshgoftaar, Amri Napolitano
2012	J	`jnl`	A System-Level Modeling Methodology for Performance-Driven Component Selection in Multicore Architectures. IEEE Syst. J. Ankur Agarwal, Georgiana L. Hamza-Lup, Taghi M. Khoshgoftaar
2012	Misc	`conf`	A novel dataset-similarity-aware approach for evaluating stability of software metric selection techniques. IRI Huanjing Wang, Taghi M. Khoshgoftaar, Randall Wald, Amri Napolitano
2012	Misc	`conf`	A review of the stability of feature selection techniques for bioinformatics data. IRI Wael Awada, Taghi M. Khoshgoftaar, David J. Dittman, Randall Wald, Amri Napolitano
2012	J	`jnl`	An Empirical Study of Feature Ranking Techniques for Software Quality Prediction. Int. J. Softw. Eng. Knowl. Eng. Taghi M. Khoshgoftaar, Kehan Gao, Amri Napolitano
2012	C	`conf`	An Empirical Study of Software Metric Selection Techniques for Defect Prediction. SEKE Huanjing Wang, Taghi M. Khoshgoftaar, Randall Wald, Amri Napolitano
2012	—	`conf`	An Empirical Study on the Stability of Feature Selection for Imbalanced Software Engineering Data. ICMLA (1) Huanjing Wang, Taghi M. Khoshgoftaar, Amri Napolitano
2012	Misc	`conf`	An extensive comparison of feature ranking aggregation techniques in bioinformatics. IRI Randall Wald, Taghi M. Khoshgoftaar, David J. Dittman, Wael Awada, Amri Napolitano
2012	—	`conf`	Applying Feature Selection to Short Time Wavelet Transformed Vibration Data for Reliability Analysis of an Ocean Turbine. ICMLA (1) Janell Duhaney, Taghi M. Khoshgoftaar, Randall Wald
2012	—	`conf`	Comparing Two New Gene Selection Ensemble Approaches with the Commonly-Used Approach. ICMLA (2) David J. Dittman, Taghi M. Khoshgoftaar, Randall Wald, Amri Napolitano
2012	—	`conf`	Decision Level Fusion of Wavelet Features for Ocean Turbine State Detection. ICMLA (2) Janell Duhaney, Taghi M. Khoshgoftaar
2012	—	`conf`	Determining the Number of Iterations Appropriate for Ensemble Gene Selection on Microarray Data. ICMLA (1) David J. Dittman, Taghi M. Khoshgoftaar, Randall Wald, Amri Napolitano
2012	J	`jnl`	Evaluation of the importance of data pre-processing order when combining feature selection and data sampling. Int. J. Bus. Intell. Data Min. Ahmad Abu Shanab, Taghi M. Khoshgoftaar, Randall Wald, Jason Van Hulse
2012	Misc	`conf`	Exploring an iterative feature selection technique for highly imbalanced data sets. IRI Taghi M. Khoshgoftaar, Kehan Gao, Amri Napolitano
2012	J	`jnl`	Exploring filter-based feature selection techniques for software quality classification. Int. J. Inf. Decis. Sci. Kehan Gao, Taghi M. Khoshgoftaar, Huanjing Wang
2012	—	`conf`	First Order Statistics Based Feature Selection: A Diverse and Powerful Family of Feature Seleciton Techniques. ICMLA (2) Taghi M. Khoshgoftaar, David J. Dittman, Randall Wald, Alireza Fazelpour
2012	Misc	`conf`	Impact of noise and data sampling on stability of feature ranking techniques for biological datasets. IRI Ahmad Abu Shanab, Taghi M. Khoshgoftaar, Randall Wald, Amri Napolitano
2012	Misc	`conf`	Machine prediction of personality from Facebook profiles. IRI Randall Wald, Taghi M. Khoshgoftaar, Chris Sumner
2012	—	`conf`	Mean Aggregation versus Robust Rank Aggregation for Ensemble Gene Selection. ICMLA (1) Randall Wald, Taghi M. Khoshgoftaar, David J. Dittman
2012	J	`jnl`	Measuring stability of feature ranking techniques: a noise-based approach. Int. J. Bus. Intell. Data Min. Wilker Altidor, Taghi M. Khoshgoftaar, Amri Napolitano
2012	Misc	`conf`	Panel: Using information re-use and integration principles in big data. IRI Gordon K. Lee, Elisa Bertino, Stuart Harvey Rubin, Taghi M. Khoshgoftaar, Bhavani Thuraisingham, James D. McCaffrey
2012	J	`jnl`	Predicting high-risk program modules by selecting the right software measurements. Softw. Qual. J. Kehan Gao, Taghi M. Khoshgoftaar, Naeem Seliya
2012	Misc	`conf`	Robustness of Threshold-Based Feature Rankers with Data Sampling on Noisy and Imbalanced Data. FLAIRS Ahmad Abu Shanab, Taghi M. Khoshgoftaar, Randall Wald
2012	—	`conf`	Similarity analysis of feature ranking techniques on imbalanced DNA microarray datasets. BIBM David J. Dittman, Taghi M. Khoshgoftaar, Randall Wald, Amri Napolitano
2012	J	`jnl`	Software measurement data reduction using ensemble techniques. Neurocomputing Huanjing Wang, Taghi M. Khoshgoftaar, Amri Napolitano
2012	C	`conf`	Stability of Filter-Based Feature Selection Methods for Imbalanced Software Measurement Data. SEKE Kehan Gao, Taghi M. Khoshgoftaar, Amri Napolitano
2012	—	`conf`	Studying the Effect of Class Imbalance in Ocean Turbine Fault Data on Reliable State Detection. ICMLA (1) Janell Duhaney, Taghi M. Khoshgoftaar, Amri Napolitano
2012	—	`conf`	The Effect of Number of Iterations on Ensemble Gene Selection. ICMLA (2) Wael Awada, Taghi M. Khoshgoftaar, David J. Dittman, Randall Wald
2012	—	`conf`	The effect of measurement approach and noise level on gene selection stability. BIBM Randall Wald, Taghi M. Khoshgoftaar, Ahmad Abu Shanab
2012	J	`jnl`	Threshold-based feature selection techniques for high-dimensional bioinformatics data. Netw. Model. Anal. Health Informatics Bioinform. Jason Van Hulse, Taghi M. Khoshgoftaar, Amri Napolitano, Randall Wald
2012	—	`conf`	Using Twitter Content to Predict Psychopathy. ICMLA (2) Randall Wald, Taghi M. Khoshgoftaar, Amri Napolitano, Chris Sumner
2011	—	`ed.`	13th IEEE International Symposium on High-Assurance Systems Engineering, HASE 2011, Boca Raton, FL, USA, November 10-12, 2011 HASE Taghi M. Khoshgoftaar
2011	C	`conf`	A Comparative Study of Different Strategies for Predicting Software Quality. SEKE Taghi M. Khoshgoftaar, Kehan Gao, Amri Napolitano
2011	—	`conf`	A Dynamometer for an Ocean Turbine Prototype: Reliability through Automated Monitoring. HASE Janell Duhaney, Taghi M. Khoshgoftaar, John C. Sloan, Bassem Alhalabi, Pierre P. Beaujean
2011	Misc	`conf`	A comparative evaluation of feature ranking methods for high dimensional bioinformatics data. IRI Jason Van Hulse, Taghi M. Khoshgoftaar, Amri Napolitano
2011	Misc	`conf`	A noise-based stability evaluation of threshold-based feature selection techniques. IRI Wilker Altidor, Taghi M. Khoshgoftaar, Amri Napolitano
2011	C	`conf`	An Empirical Study of Software Metrics Selection Using Support Vector Machine. SEKE Huanjing Wang, Taghi M. Khoshgoftaar, Amri Napolitano
2011	J	`jnl`	An exploration of learning when data is noisy and imbalanced. Intell. Data Anal. Jason Van Hulse, Taghi M. Khoshgoftaar, Amri Napolitano
2011	J	`jnl`	Choosing software metrics for defect prediction: an investigation on feature selection techniques. Softw. Pract. Exp. Kehan Gao, Taghi M. Khoshgoftaar, Huanjing Wang, Naeem Seliya
2011	J	`jnl`	Comparing Boosting and Bagging Techniques With Noisy and Imbalanced Data. IEEE Trans. Syst. Man Cybern. Part A Taghi M. Khoshgoftaar, Jason Van Hulse, Amri Napolitano
2011	Misc	`conf`	Comparison of approaches to alleviate problems with high-dimensional and class-imbalanced data. IRI Ahmad Abu Shanab, Taghi M. Khoshgoftaar, Randall Wald, Jason Van Hulse
2011	—	`conf`	Ensemble Coordination for Discrete Event Control. HASE John C. Sloan, Taghi M. Khoshgoftaar
2011	J	`jnl`	Evaluating the Impact of Data Quality on Sampling. J. Inf. Knowl. Manag. Jason Van Hulse, Taghi M. Khoshgoftaar, Amri Napolitano
2011	Misc	`conf`	Feature Level Sensor Fusion for Improved Fault Detection in MCM Systems for Ocean Turbines. FLAIRS Janell Duhaney, Taghi M. Khoshgoftaar, John C. Sloan
2011	B	`conf`	Feature Selection for Vibration Sensor Data Transformed by a Streaming Wavelet Packet Decomposition. ICTAI Randall Wald, Taghi M. Khoshgoftaar, John C. Sloan
2011	B	`conf`	Feature Selection on Dynamometer Data for Reliability Analysis. ICTAI Janell Duhaney, Taghi M. Khoshgoftaar, John C. Sloan
2011	Misc	`conf`	Fourier transforms for vibration analysis: A review and case study. IRI Randall Wald, Taghi M. Khoshgoftaar, John C. Sloan
2011	Misc	`conf`	How Many Software Metrics Should be Selected for Defect Prediction? FLAIRS Huanjing Wang, Taghi M. Khoshgoftaar, Naeem Seliya
2011	J	`jnl`	Identification of microRNA biomarkers for cancer by combining multiple feature selection techniques. J. Comput. Methods Sci. Eng. Alex Kotlarchyk, Taghi M. Khoshgoftaar, Mirjana Pavlovic, Hanqi Zhuang, Abhijit S. Pandya
2011	B	`conf`	Impact of Data Sampling on Stability of Feature Selection for Software Measurement Data. ICTAI Kehan Gao, Taghi M. Khoshgoftaar, Amri Napolitano
2011	—	`conf`	Impact of Noise and Data Sampling on Stability of Feature Selection. ICMLA (1) Ahmad Abu Shanab, Taghi M. Khoshgoftaar, Randall Wald
2011	B	`conf`	Measuring Stability of Threshold-Based Feature Selection Techniques. ICTAI Huanjing Wang, Taghi M. Khoshgoftaar
2011	Misc	`conf`	Measuring robustness of Feature Selection techniques on software engineering datasets. IRI Huanjing Wang, Taghi M. Khoshgoftaar, Randall Wald
2011	J	`jnl`	Metric Selection for Software Defect Prediction. Int. J. Softw. Eng. Knowl. Eng. Huanjing Wang, Taghi M. Khoshgoftaar, Jason Van Hulse, Kehan Gao
2011	J	`jnl`	Ontology-Based Business Process Customization for Composite Web Services. IEEE Trans. Syst. Man Cybern. Part A Qianhui Liang, Xindong Wu, E. K. Park, Taghi M. Khoshgoftaar, Chi-Hung Chi
2011	—	`conf`	Random forest: A reliable tool for patient response prediction. BIBM Workshops David J. Dittman, Taghi M. Khoshgoftaar, Randall Wald, Amri Napolitano
2011	Misc	`conf`	Robustness of Filter-Based Feature Ranking: A Case Study. FLAIRS Wilker Altidor, Taghi M. Khoshgoftaar, Jason Van Hulse
2011	C	`conf`	Software Defect Prediction for High-Dimensional and Class-Imbalanced Data. SEKE Kehan Gao, Taghi M. Khoshgoftaar
2011	—	`conf`	Stability Analysis of Feature Ranking Techniques on Biological Datasets. BIBM David J. Dittman, Taghi M. Khoshgoftaar, Randall Wald, Huanjing Wang
2011	—	`conf`	Stability and Classification Performance of Feature Selection Techniques. ICMLA (1) Huanjing Wang, Taghi M. Khoshgoftaar, Qianhui Althea Liang
2011	J	`jnl`	The use of decision trees for cost-sensitive classification: an empirical study in software quality prediction. WIREs Data Mining Knowl. Discov. Naeem Seliya, Taghi M. Khoshgoftaar
2011	—	`conf`	Using Classifier-Based Nominal Imputation to Improve Machine Learning. PAKDD (1) Xiaoyuan Su, Russell Greiner, Taghi M. Khoshgoftaar, Amri Napolitano
2011	—	`conf`	Using Feature Selection to Determine Optimal Depth for Wavelet Packet Decomposition of Vibration Signals for Ocean System Reliability. HASE Randall Wald, Taghi M. Khoshgoftaar, John C. Sloan
2010	C	`conf`	A Comparative Study of Ensemble Feature Selection Techniques for Software Defect Prediction. ICMLA Huanjing Wang, Taghi M. Khoshgoftaar, Amri Napolitano
2010	Misc	`conf`	A Comparative Study of Threshold-Based Feature Selection Techniques. GrC Huanjing Wang, Taghi M. Khoshgoftaar, Jason Van Hulse
2010	C	`conf`	A Novel Noise Filtering Algorithm for Imbalanced Data. ICMLA Jason Van Hulse, Taghi M. Khoshgoftaar, Amri Napolitano
2010	Misc	`conf`	A comparative study of filter-based feature ranking techniques. IRI Huanjing Wang, Taghi M. Khoshgoftaar, Kehan Gao
2010	Misc	`conf`	A novel feature selection technique for highly imbalanced data. IRI Taghi M. Khoshgoftaar, Kehan Gao, Jason Van Hulse
2010	Misc	`conf`	Active learning with neural networks for intrusion detection. IRI Naeem Seliya, Taghi M. Khoshgoftaar
2010	J	`jnl`	An Empirical Evaluation of Repetitive Undersampling Techniques. Int. J. Softw. Eng. Knowl. Eng. Jason Van Hulse, Taghi M. Khoshgoftaar, Amri Napolitano
2010	—	`conf`	An Empirical Study of Predictive Modeling Techniques of Software Quality. BIONETICS Taghi M. Khoshgoftaar, Kehan Gao, Amri Napolitano
2010	Misc	`conf`	An Evaluation of Sampling on Filter-Based Feature Selection Methods. FLAIRS Kehan Gao, Taghi M. Khoshgoftaar, Jason Van Hulse
2010	—	`conf`	Attribute Selection and Imbalanced Data: Problems in Software Defect Prediction. ICTAI (1) Taghi M. Khoshgoftaar, Kehan Gao, Naeem Seliya
2010	C	`conf`	Comparative Analysis of DNA Microarray Data through the Use of Feature Selection Techniques. ICMLA David J. Dittman, Taghi M. Khoshgoftaar, Randall Wald, Jason Van Hulse
2010	J	`jnl`	Dynamic Two-phase Truncated Rayleigh Model for Release Date Prediction of Software. J. Softw. Eng. Appl. Lianfen Qian, Qingchuan Yao, Taghi M. Khoshgoftaar
2010	C	`conf`	Ensemble Feature Selection Technique for Software Quality Classification. SEKE Huanjing Wang, Taghi M. Khoshgoftaar, Kehan Gao
2010	Misc	`conf`	Evaluating the impact of data quality on sampling. IRI Jason Van Hulse, Taghi M. Khoshgoftaar, Amri Napolitano
2010	J	`jnl`	Evolutionary Optimization of Software Quality Modeling with Multiple Repositories. IEEE Trans. Software Eng. Yi Liu, Taghi M. Khoshgoftaar, Naeem Seliya
2010	J	`jnl`	Evolutionary data analysis for the class imbalance problem. Intell. Data Anal. Taghi M. Khoshgoftaar, Naeem Seliya, Dennis J. Drown
2010	—	`conf`	Predicting Faults in High Assurance Software. HASE Naeem Seliya, Taghi M. Khoshgoftaar, Jason Van Hulse
2010	J	`jnl`	RUSBoost: A Hybrid Approach to Alleviating Class Imbalance. IEEE Trans. Syst. Man Cybern. Part A Chris Seiffert, Taghi M. Khoshgoftaar, Jason Van Hulse, Amri Napolitano
2010	C	`conf`	Software Engineering with Computational Intelligence and Machine Learning A Novel Software Metric Selection Technique Using the Area Under ROC Curves. SEKE Taghi M. Khoshgoftaar, Kehan Gao
2010	J	`jnl`	Supervised neural network modeling: an empirical investigation into learning from imbalanced data with labeling errors. IEEE Trans. Neural Networks Taghi M. Khoshgoftaar, Jason Van Hulse, Amri Napolitano
2010	C	`ed.`	The Ninth International Conference on Machine Learning and Applications, ICMLA 2010, Washington, DC, USA, 12-14 December 2010 ICMLA Sorin Draghici, Taghi M. Khoshgoftaar, Vasile Palade, Witold Pedrycz, M. Arif Wani, Xingquan Zhu
2009	C	`conf`	A Novel Hybrid Search Algorithm for Feature Selection. SEKE Pengpeng Lin, Huanjing Wang, Taghi M. Khoshgoftaar
2009	B	`conf`	A Study on the Relationships of Classifier Performance Metrics. ICTAI Naeem Seliya, Taghi M. Khoshgoftaar, Jason Van Hulse
2009	J	`jnl`	A Survey of Collaborative Filtering Techniques. Adv. Artif. Intell. Xiaoyuan Su, Taghi M. Khoshgoftaar
2009	Misc	`conf`	Aggregating Performance Metrics for Classifier Evaluation. IRI Naeem Seliya, Taghi M. Khoshgoftaar, Jason Van Hulse
2009	Misc	`conf`	An Empirical Comparison of Repetitive Undersampling Techniques. IRI Jason Van Hulse, Taghi M. Khoshgoftaar, Amri Napolitano
2009	Misc	`conf`	An Empirical Investigation of Filter Attribute Selection Techniques for Software Quality Classification. IRI Kehan Gao, Taghi M. Khoshgoftaar, Huanjing Wang
2009	B	`conf`	An Empirical Study on Wrapper-Based Feature Ranking. ICTAI Wilker Altidor, Taghi M. Khoshgoftaar, Jason Van Hulse
2009	C	`conf`	An Extendible Translation of BPEL to a Machine-verifiable Model. SEKE John C. Sloan, Taghi M. Khoshgoftaar, Augusto Varas
2009	—	`ch.`	Count Models for Software Quality Estimation. Encyclopedia of Data Warehousing and Mining Kehan Gao, Taghi M. Khoshgoftaar
2009	J	`jnl`	Empirical Case Studies in Attribute Noise Detection. IEEE Trans. Syst. Man Cybern. Part C Taghi M. Khoshgoftaar, Jason Van Hulse
2009	J	`jnl`	Evolutionary Sampling and Software Quality Modeling of High-Assurance Systems. IEEE Trans. Syst. Man Cybern. Part A Dennis J. Drown, Taghi M. Khoshgoftaar, Naeem Seliya
2009	B	`conf`	Exploring Software Quality Classification with a Wrapper-Based Feature Ranking Technique. ICTAI Kehan Gao, Taghi M. Khoshgoftaar, Amri Napolitano
2009	—	`conf`	Feature Selection with High-Dimensional Imbalanced Data. ICDM Workshops Jason Van Hulse, Taghi M. Khoshgoftaar, Amri Napolitano, Randall Wald
2009	C	`conf`	Feature Selection with Imbalanced Data for Software Defect Prediction. ICMLA Taghi M. Khoshgoftaar, Kehan Gao
2009	J	`jnl`	From Web Service Artifact to a Readable and Verifiable Model. IEEE Trans. Serv. Comput. John C. Sloan, Taghi M. Khoshgoftaar
2009	B	`conf`	High-Dimensional Software Engineering Data and Feature Selection. ICTAI Huanjing Wang, Taghi M. Khoshgoftaar, Kehan Gao, Naeem Seliya
2009	J	`jnl`	Hybrid sampling for imbalanced data. Integr. Comput. Aided Eng. Chris Seiffert, Taghi M. Khoshgoftaar, Jason Van Hulse
2009	J	`jnl`	Identifying Learners Robust to Low Quality Data. Informatica (Slovenia) Andres Folleco, Taghi M. Khoshgoftaar, Jason Van Hulse, Amri Napolitano
2009	J	`jnl`	Improving Software-Quality Predictions With Data Sampling and Boosting. IEEE Trans. Syst. Man Cybern. Part A Chris Seiffert, Taghi M. Khoshgoftaar, Jason Van Hulse
2009	J	`jnl`	Knowledge discovery from imbalanced and noisy data. Data Knowl. Eng. Jason Van Hulse, Taghi M. Khoshgoftaar
2009	J	`jnl`	Making an accurate classifier ensemble by voting on classifications from imputed learning sets. Int. J. Inf. Decis. Sci. Xiaoyuan Su, Taghi M. Khoshgoftaar, Russell Greiner
2009	—	`conf`	Mining Data from Multiple Software Development Projects. ICDM Workshops Huanjing Wang, Taghi M. Khoshgoftaar, Kehan Gao, Naeem Seliya
2009	J	`jnl`	Software quality analysis by combining multiple projects and learners. Softw. Qual. J. Taghi M. Khoshgoftaar, Pierre Rebours, Naeem Seliya
2009	C	`conf`	Value-Based Software Quality Modeling. SEKE Naeem Seliya, Taghi M. Khoshgoftaar
2009	Misc	`conf`	VipBoost: A More Accurate Boosting Algorithm. FLAIRS Xiaoyuan Su, Taghi M. Khoshgoftaar, Russell Greiner
2009	C	`conf`	Wrapper-Based Feature Ranking for Software Engineering Metrics. ICMLA Wilker Altidor, Taghi M. Khoshgoftaar, Amri Napolitano
2008	—	`conf`	A Comparative Study of Data Sampling and Cost Sensitive Learning. ICDM Workshops Chris Seiffert, Taghi M. Khoshgoftaar, Jason Van Hulse, Amri Napolitano
2008	Misc	`conf`	A Mixture Imputation-Boosted Collaborative Filter. FLAIRS Xiaoyuan Su, Taghi M. Khoshgoftaar, Russell Greiner
2008	J	`jnl`	A comprehensive empirical evaluation of missing value imputation in noisy software measurement data. J. Syst. Softw. Jason Van Hulse, Taghi M. Khoshgoftaar
2008	—	`conf`	Addressing Class Imbalance in Non-binary Classification Problems. ICTAI (1) Naeem Seliya, Zhiwei Xu, Taghi M. Khoshgoftaar
2008	C	`conf`	Analyzing the Impact of Attribute Noise on Software Quality Classification. SEKE Andres Folleco, Taghi M. Khoshgoftaar, Lofton A. Bullard
2008	J	`jnl`	Assuring Timeliness in an e-Science Service-Oriented Architecture. Computer John C. Sloan, Taghi M. Khoshgoftaar, Venkat Raghav
2008	Misc	`conf`	Building Useful Models from Imbalanced Data with Sampling and Boosting. FLAIRS Chris Seiffert, Taghi M. Khoshgoftaar, Jason Van Hulse, Amri Napolitano
2008	J	`jnl`	Collaborative Filtering for Multi-Class Data Using Bayesian Networks. Int. J. Artif. Intell. Tools Xiaoyuan Su, Taghi M. Khoshgoftaar
2008	C	`conf`	Comparison of Four Performance Metrics for Evaluating Sampling Techniques for Low Quality Class-Imbalanced Data. ICMLA Andres Folleco, Taghi M. Khoshgoftaar, Amri Napolitano
2008	Misc	`conf`	Contrast Pattern Mining with Gap Constraints for Peptide Folding Prediction. FLAIRS Chinar C. Shah, Xingquan Zhu, Taghi M. Khoshgoftaar, Justin Beyer
2008	Misc	`conf`	Hybrid sampling for imbalanced data. IRI Chris Seiffert, Taghi M. Khoshgoftaar, Jason Van Hulse
2008	Misc	`conf`	Identifying learners robust to low quality data. IRI Andres Folleco, Taghi M. Khoshgoftaar, Jason Van Hulse, Lofton A. Bullard
2008	—	`conf`	Improving Learner Performance with Data Sampling and Boosting. ICTAI (1) Chris Seiffert, Taghi M. Khoshgoftaar, Jason Van Hulse, Amri Napolitano
2008	J	`jnl`	Imputation techniques for multivariate missingness in software measurement data. Softw. Qual. J. Taghi M. Khoshgoftaar, Jason Van Hulse
2008	Misc	`conf`	Imputation-boosted collaborative filtering using machine learning classifiers. SAC Xiaoyuan Su, Taghi M. Khoshgoftaar, Xingquan Zhu, Russell Greiner
2008	—	`conf`	Imputed Neighborhood Based Collaborative Filtering. Web Intelligence Xiaoyuan Su, Taghi M. Khoshgoftaar, Russell Greiner
2008	J	`jnl`	Low-Effort Labeling of Network Events for Intrusion Detection in WLANs. Int. J. Artif. Intell. Tools Taghi M. Khoshgoftaar, Naeem Seliya, Chris Seiffert
2008	C	`conf`	On the Rarity of Fault-prone Modules in Knowledge-based Software Quality Modeling. SEKE Taghi M. Khoshgoftaar, Naeem Seliya, Dennis J. Drown
2008	B	`conf`	RUSBoost: Improving classification performance when training data is skewed. ICPR Chris Seiffert, Taghi M. Khoshgoftaar, Jason Van Hulse, Amri Napolitano
2008	—	`conf`	Resampling or Reweighting: A Comparison of Boosting Implementations. ICTAI (1) Chris Seiffert, Taghi M. Khoshgoftaar, Jason Van Hulse, Amri Napolitano
2008	—	`ch.`	Software Module Risk Analysis. Wiley Encyclopedia of Computer Science and Engineering Taghi M. Khoshgoftaar, Naeem Seliya
2008	—	`ch.`	Software Quality Modeling as a Reliability Tool. Wiley Encyclopedia of Computer Science and Engineering Naeem Seliya, Taghi M. Khoshgoftaar
2008	B	`conf`	Software quality modeling: The impact of class noise on the random forest classifier. IEEE Congress on Evolutionary Computation Andres Folleco, Taghi M. Khoshgoftaar, Jason Van Hulse, Lofton A. Bullard
2008	C	`conf`	Toward Model Checking Web Services Over the Web. SEKE John C. Sloan, Taghi M. Khoshgoftaar
2008	—	`conf`	Using Imputation Techniques to Help Learn Accurate Classifiers. ICTAI (1) Xiaoyuan Su, Taghi M. Khoshgoftaar, Russell Greiner
2008	Misc	`conf`	VCI predictors: Voting on classifications from imputed learning sets. IRI Xiaoyuan Su, Taghi M. Khoshgoftaar, Xingquan Zhu
2008	B	`conf`	VoB predictors: Voting on bagging classifications. ICPR Xiaoyuan Su, Taghi M. Khoshgoftaar, Xingquan Zhu
2007	J	`jnl`	A Comprehensive Empirical Study of Count Models for Software Fault Prediction. IEEE Trans. Reliab. Kehan Gao, Taghi M. Khoshgoftaar
2007	J	`jnl`	A Multi-Objective Software Quality Classification Model Using Genetic Programming. IEEE Trans. Reliab. Taghi M. Khoshgoftaar, Yi Liu
2007	—	`conf`	A Progressive Edge-Based Stereo Correspondence Method. ISVC (1) Xiaoyuan Su, Taghi M. Khoshgoftaar
2007	—	`conf`	An Empirical Study of Learning from Imbalanced Data Using Random Forest. ICTAI (2) Taghi M. Khoshgoftaar, Moiz Golawala, Jason Van Hulse
2007	Misc	`conf`	An Empirical Study of the Classification Performance of Learners on Imbalanced and Noisy Software Quality Data. IRI Chris Seiffert, Taghi M. Khoshgoftaar, Jason Van Hulse, Andres Folleco
2007	A*	`conf`	An Empirical Study of the Noise Impact on Cost-Sensitive Learning. IJCAI Xingquan Zhu, Xindong Wu, Taghi M. Khoshgoftaar, Yong Shi
2007	Misc	`conf`	An Empirical Study on Estimating Motions in Video Stabilization. IRI Qiming Luo, Taghi M. Khoshgoftaar
2007	C	`conf`	An application of a rule-based model in software quality classification. ICMLA Lofton A. Bullard, Taghi M. Khoshgoftaar, Kehan Gao
2007	—	`conf`	Arbitrarily-Shaped Window Based Stereo Matching using the Go-Light Optimization Algorithm. ICIP (6) Xiaoyuan Su, Taghi M. Khoshgoftaar
2007	Misc	`conf`	Building a Novel GP-Based Software Quality Classifier Using Multiple Validation Datasets. IRI Yi Liu, Taghi M. Khoshgoftaar, Jenq-Foung JF Yao
2007	J	`jnl`	Count Models for Software Quality Estimation. IEEE Trans. Reliab. Taghi M. Khoshgoftaar, Kehan Gao
2007	J	`jnl`	Editorial: Special issue on mining low-quality data. Knowl. Inf. Syst. Xingquan Zhu, Taghi M. Khoshgoftaar, Ian Davidson, Shichao Zhang
2007	A*	`conf`	Experimental perspectives on learning from imbalanced data. ICML Jason Van Hulse, Taghi M. Khoshgoftaar, Amri Napolitano
2007	—	`conf`	Hybrid Collaborative Filtering Algorithms Using a Mixture of Experts. Web Intelligence Xiaoyuan Su, Russell Greiner, Taghi M. Khoshgoftaar, Xingquan Zhu
2007	J	`jnl`	Improving Software Quality Prediction by Noise Filtering Techniques. J. Comput. Sci. Technol. Taghi M. Khoshgoftaar, Pierre Rebours
2007	Misc	`conf`	Incomplete-Case Nearest Neighbor Imputation in Software Measurement Data. IRI Jason Van Hulse, Taghi M. Khoshgoftaar
2007	C	`conf`	Learning from Software Quality Data with Class Imbalance and Noise. SEKE Andres Folleco, Taghi M. Khoshgoftaar, Jason Van Hulse, Chris Seiffert
2007	C	`conf`	Learning with limited minority class data. ICMLA Taghi M. Khoshgoftaar, Chris Seiffert, Jason Van Hulse, Amri Napolitano, Andres Folleco
2007	Misc	`conf`	Low-Effort Labeling of Network Events for Intrusion Detection in WLANs. FLAIRS Taghi M. Khoshgoftaar, Chris Seiffert, Naeem Seliya
2007	—	`conf`	Mining Data with Rare Events: A Case Study. ICTAI (2) Chris Seiffert, Taghi M. Khoshgoftaar, Jason Van Hulse, Amri Napolitano
2007	—	`conf`	Rule-Based Multiple Object Tracking for Traffic Surveillance Using Collaborative Background Extraction. ISVC (2) Xiaoyuan Su, Taghi M. Khoshgoftaar, Xingquan Zhu, Andres Folleco
2007	—	`conf`	Skewed Class Distributions and Mislabeled Examples. ICDM Workshops Jason Van Hulse, Taghi M. Khoshgoftaar, Amri Napolitano
2007	J	`jnl`	Software Quality Analysis of Unlabeled Program Modules With Semisupervised Clustering. IEEE Trans. Syst. Man Cybern. Part A Naeem Seliya, Taghi M. Khoshgoftaar
2007	J	`jnl`	Software quality estimation with limited fault data: a semi-supervised learning perspective. Softw. Qual. J. Naeem Seliya, Taghi M. Khoshgoftaar
2007	J	`jnl`	The multiple imputation quantitative noise corrector. Intell. Data Anal. Taghi M. Khoshgoftaar, Jason Van Hulse, Chris Seiffert, Lili Zhao
2007	J	`jnl`	The pairwise attribute noise detection algorithm. Knowl. Inf. Syst. Jason Van Hulse, Taghi M. Khoshgoftaar, Haiying Huang
2007	C	`conf`	Using evolutionary sampling to mine imbalanced data. ICMLA Dennis J. Drown, Taghi M. Khoshgoftaar, Ramaswamy Narayanan
2006	C	`conf`	A Comparison of Software Fault Imputation Procedures. ICMLA Jason Van Hulse, Taghi M. Khoshgoftaar, Chris Seiffert
2006	B	`conf`	A Hybrid Approach to Cleansing Software Measurement Data. ICTAI Taghi M. Khoshgoftaar, Jason Van Hulse, Chris Seiffert
2006	J	`jnl`	An empirical study of predicting software faults with case-based reasoning. Softw. Qual. J. Taghi M. Khoshgoftaar, Naeem Seliya, Nandini Sundaresh
2006	B	`conf`	Assessment of a Multi-Strategy Classifier for an Embedded Software System. ICTAI Taghi M. Khoshgoftaar, Kehan Gao
2006	J	`jnl`	Class noise detection using frequent itemsets. Intell. Data Anal. Jason Van Hulse, Taghi M. Khoshgoftaar
2006	Misc	`conf`	Classification of ships in surveillance video. IRI Qiming Luo, Taghi M. Khoshgoftaar, Andres Folleco
2006	B	`conf`	Collaborative Filtering for Multi-class Data Using Belief Nets Algorithms. ICTAI Xiaoyuan Su, Taghi M. Khoshgoftaar
2006	J	`jnl`	Detecting Noisy Instances with the Ensemble Filter: a Study in Software Quality Estimation. Int. J. Softw. Eng. Knowl. Eng. Taghi M. Khoshgoftaar, Vedang H. Joshi, Naeem Seliya
2006	J	`jnl`	Determining noisy instances relative to attributes of interest. Intell. Data Anal. Taghi M. Khoshgoftaar, Jason Van Hulse
2006	Misc	`conf`	Developing an effective validation strategy for genetic programming models based on multiple datasets. IRI Yi Liu, Taghi M. Khoshgoftaar, Jenq-Foung JF Yao
2006	J	`jnl`	Indirect classification approaches: a comparative study in network intrusion detection. Int. J. Comput. Appl. Technol. Taghi M. Khoshgoftaar, Kehan Gao, Hua Lin
2006	Misc	`conf`	Labeling network event records for intrusion detection in a Wireless LAN. IRI Taghi M. Khoshgoftaar, Chris Seiffert, Naeem Seliya
2006	C	`conf`	Multiple Imputation of Software Measurement Data: A Case Study. SEKE Taghi M. Khoshgoftaar, Jason Van Hulse
2006	Misc	`conf`	Noise correction using bayesian multiple imputation. IRI Jason Van Hulse, Taghi M. Khoshgoftaar, Chris Seiffert, Lili Zhao
2006	J	`jnl`	Noise elimination with partitioning filter for software quality estimation. Int. J. Comput. Appl. Technol. Taghi M. Khoshgoftaar, Pierre Rebours
2006	C	`conf`	Polishing Noise in Continuous Software Measurement Data. SEKE Taghi M. Khoshgoftaar, Chris Seiffert, Jason Van Hulse
2006	J	`jnl`	Quality Problem in Software Measurement Data. Adv. Comput. Pierre Rebours, Taghi M. Khoshgoftaar
2006	J	`jnl`	Resource oriented selection of rule-based classification models: An empirical case study. Softw. Qual. J. Taghi M. Khoshgoftaar, Angela Herzberg, Naeem Seliya
2006	Misc	`conf`	Software quality imputation in the presence of noisy data. IRI Taghi M. Khoshgoftaar, Andres Folleco, Jason Van Hulse, Lofton A. Bullard
2006	J	`jnl`	Unsupervised multiscale color image segmentation based on MDL principle. IEEE Trans. Image Process. Qiming Luo, Taghi M. Khoshgoftaar
2005	B	`conf`	A Clustering Approach to Wireless Network Intrusion Detection. ICTAI Shi Zhong, Taghi M. Khoshgoftaar, Shyam Varan Nath
2005	—	`conf`	Analyzing Software Quality with Limited Fault-Proneness Defect Data. HASE Naeem Seliya, Taghi M. Khoshgoftaar, Shi Zhong
2005	Misc	`conf`	Application of fuzzy expert system in test case selection for system regression test. IRI Zhiwei Xu, Kehan Gao, Taghi M. Khoshgoftaar
2005	J	`jnl`	Assessment of a New Three-Group Software Quality Classification Technique: An Empirical Case Study. Empir. Softw. Eng. Taghi M. Khoshgoftaar, Naeem Seliya, Kehan Gao
2005	J	`jnl`	Comparing software fault predictions of pure and zero-inflated Poisson regression models. Int. J. Syst. Sci. Taghi M. Khoshgoftaar, Kehan Gao, Robert M. Szabo
2005	J	`jnl`	Detecting noisy instances with the rule-based classification model. Intell. Data Anal. Taghi M. Khoshgoftaar, Naeem Seliya, Kehan Gao
2005	Misc	`conf`	Empirical case studies in attribute noise detection. IRI Taghi M. Khoshgoftaar, Jason Van Hulse
2005	J	`jnl`	Enhancing software quality estimation using ensemble-classifier based noise filtering. Intell. Data Anal. Taghi M. Khoshgoftaar, Shi Zhong, Vedang H. Joshi
2005	J	`jnl`	Evaluating indirect and direct classification techniques for network intrusion detection. Intell. Data Anal. Taghi M. Khoshgoftaar, Kehan Gao, Nawal H. Ibrahim
2005	J	`jnl`	Evaluating noise elimination techniques for software quality estimation. Intell. Data Anal. Taghi M. Khoshgoftaar, Pierre Rebours
2005	Misc	`conf`	Hierarchical indexing of ocean survey video by mean shift clustering and MDL principle. IRI Qiming Luo, Taghi M. Khoshgoftaar, Edgar An
2005	C	`conf`	Identifying noise in an attribute of interest. ICMLA Taghi M. Khoshgoftaar, Jason Van Hulse
2005	J	`jnl`	Identifying noisy features with the Pairwise Attribute Noise Detection Algorithm. Intell. Data Anal. Taghi M. Khoshgoftaar, Jason Van Hulse
2005	C	`conf`	Intrusion detection in wireless networks using clustering techniques with expert analysis. ICMLA Taghi M. Khoshgoftaar, Shyam Varan Nath, Shi Zhong, Naeem Seliya
2005	Misc	`ed.`	Proceedings of the 2005 IEEE International Conference on Information Reuse and Integration, IRI - 2005, August 15-17, 2005, Las Vegas Hilton, Las Vegas, NV, USA IRI Du Zhang, Taghi M. Khoshgoftaar, Mei-Ling Shyu
2005	J	`jnl`	Resource-oriented software quality classification models. J. Syst. Softw. Taghi M. Khoshgoftaar, Naeem Seliya, Angela Herzberg
2005	Misc	`conf`	The partitioning- and rule-based filter for noise detection. IRI Yudong Xiao, Taghi M. Khoshgoftaar, Naeem Seliya
2004	J	`jnl`	A multiobjective module-order model for software quality enhancement. IEEE Trans. Evol. Comput. Taghi M. Khoshgoftaar, Yi Liu, Naeem Seliya
2004	J	`jnl`	Analyzing Software Measurement Data with Clustering Techniques. IEEE Intell. Syst. Shi Zhong, Taghi M. Khoshgoftaar, Naeem Seliya
2004	J	`jnl`	Comparative Assessment of Software Quality Classification Techniques: An Empirical Case Study. Empir. Softw. Eng. Taghi M. Khoshgoftaar, Naeem Seliya
2004	B	`conf`	Efficient Image Segmentation by Mean Shift Clustering and MDL-Guided Region Merging. ICTAI Qiming Luo, Taghi M. Khoshgoftaar
2004	Misc	`conf`	Generating Multiple Noise Elimination Filters with the Ensemble-Partitioning Filter. IRI Taghi M. Khoshgoftaar, Pierre Rebours
2004	J	`jnl`	Identification of fuzzy models of software cost estimation. Fuzzy Sets Syst. Zhiwei Xu, Taghi M. Khoshgoftaar
2004	—	`conf`	Module-Order Modeling using an Evolutionary Multi-Objective Optimization Approach. IEEE METRICS Taghi M. Khoshgoftaar, Yi Liu, Naeem Seliya
2004	C	`conf`	Multi-Objective Optimization by CBR GA-Optimizer for Module-Order Modeling. SEKE Taghi M. Khoshgoftaar, Yudong Xiao, Kehan Gao
2004	C	`conf`	Noise Elimination with Ensemble-Classifier Filtering: A Case-Study in Software Quality Engineerin. SEKE Taghi M. Khoshgoftaar, Vedang H. Joshi
2004	B	`conf`	Noise Identification with the k-Means Algorithm. ICTAI Wei Tang, Taghi M. Khoshgoftaar
2004	—	`conf`	Reducing Overfitting in Genetic Programming Models for Software Quality Classification. HASE Yi Liu, Taghi M. Khoshgoftaar
2004	—	`conf`	Resource-Sensitive Intrusion Detection Models for Network Traffic. HASE Taghi M. Khoshgoftaar, Mohamed E. Abushadi
2004	Misc	`conf`	Rule-Based Noise Detection for Software Measurement Data. IRI Taghi M. Khoshgoftaar, Naeem Seliya, Kehan Gao
2004	B	`conf`	Semi-Supervised Learning for Software Quality Estimation. ICTAI Naeem Seliya, Taghi M. Khoshgoftaar, Shi Zhong
2004	J	`jnl`	Software quality estimation with case-based reasoning. Adv. Comput. Taghi M. Khoshgoftaar, Naeem Seliya
2004	—	`conf`	The Necessity of Assuring Quality in Software Measurement Data. IEEE METRICS Taghi M. Khoshgoftaar, Naeem Seliya
2004	—	`conf`	Unsupervised Learning for Expert-Based Software Quality Estimation. HASE Shi Zhong, Taghi M. Khoshgoftaar, Naeem Seliya
2003	J	`jnl`	Analogy-Based Practical Classification Rules for Software Quality Estimation. Empir. Softw. Eng. Taghi M. Khoshgoftaar, Naeem Seliya
2003	B	`conf`	Application of an Attribute Selection Method to CBR-Based Software Quality Classification. ICTAI Taghi M. Khoshgoftaar, Laurent A. Nguyen, Kehan Gao, Jayanth Rajeevalochanam
2003	J	`jnl`	Application of fuzzy expert systems in assessing operational risk of software. Inf. Softw. Technol. Zhiwei Xu, Taghi M. Khoshgoftaar, Edward B. Allen
2003	A	`conf`	Building Decision Tree Software Quality Classification Models Using Genetic Programming. GECCO Yi Liu, Taghi M. Khoshgoftaar
2003	C	`conf`	Detecting Outliers Using Rule-Based Modeling for Improving CBR-Based Software Quality Classification Models. ICCBR Taghi M. Khoshgoftaar, Lofton A. Bullard, Kehan Gao
2003	—	`conf`	Empirical Case Studies of Combining Software Quality Classification Models. QSIC Taghi M. Khoshgoftaar, Erik Geleyn, Laurent A. Nguyen
2003	J	`jnl`	Fault Prediction Modeling for Software Quality Estimation: Comparing Commonly Used Techniques. Empir. Softw. Eng. Taghi M. Khoshgoftaar, Naeem Seliya
2003	B	`conf`	Genetic Programming-Based Decision Trees for Software Quality Classification. ICTAI Taghi M. Khoshgoftaar, Yi Liu, Naeem Seliya
2003	J	`jnl`	Introduction to the Special Issue on Quality Engineering with Computational Intelligence. Softw. Qual. J. Taghi M. Khoshgoftaar
2003	J	`jnl`	Ordering Fault-Prone Software Modules. Softw. Qual. J. Taghi M. Khoshgoftaar, Edward B. Allen
2003	J	`jnl`	Software Quality Classification Modeling Using the SPRINT Decision Tree Algorithm. Int. J. Artif. Intell. Tools Taghi M. Khoshgoftaar, Naeem Seliya
2002	—	`conf`	An Empirical Study of the Impact of Count Models Predictions on Module-Order Models. IEEE METRICS Taghi M. Khoshgoftaar, Erik Geleyn, Kehan Gao
2002	B	`conf`	Can neural networks be easily interpreted in software cost estimation? FUZZ-IEEE Ali Idri, Taghi M. Khoshgoftaar, Alain Abran
2002	—	`conf`	Cost-Sensitive Boosting In Software Quality Modeling. HASE Taghi M. Khoshgoftaar, Erik Geleyn, Laurent A. Nguyen, Lofton A. Bullard
2002	—	`conf`	Estimating Software Project Effort by Analogy Based on Linguistic Values. IEEE METRICS Ali Idri, Alain Abran, Taghi M. Khoshgoftaar
2002	A	`conf`	Improving Usefulness of Software Quality Classification Models Based on Boolean Discriminant Functions. ISSRE Taghi M. Khoshgoftaar
2002	J	`jnl`	Predicting Fault-Prone Modules in Embedded Systems Using Analogy-Based Classification Models. Int. J. Softw. Eng. Knowl. Eng. Taghi M. Khoshgoftaar, Bojan Cukic, Naeem Seliya
2002	B	`conf`	Software Quality Classification Modeling Using The SPRINT Decision Tree Algorithm. ICTAI Taghi M. Khoshgoftaar, Naeem Seliya
2002	—	`conf`	Tree-Based Software Quality Estimation Models For Fault Prediction. IEEE METRICS Taghi M. Khoshgoftaar, Naeem Seliya
2002	J	`jnl`	Uncertain Classification of Fault-Prone Software Modules. Empir. Softw. Eng. Taghi M. Khoshgoftaar, Xiaojing Yuan, Edward B. Allen, Wendell D. Jones, John P. Hudepohl
2002	J	`jnl`	Using regression trees to classify fault-prone software modules. IEEE Trans. Reliab. Taghi M. Khoshgoftaar, Edward B. Allen, Jianyu Deng
2001	A	`conf`	An Application of Zero-Inflated Poisson Regression for Software Fault Prediction . ISSRE Taghi M. Khoshgoftaar, Kehan Gao, Robert M. Szabo
2001	J	`jnl`	Controlling Overfitting in Classification-Tree Models of Software Quality. Empir. Softw. Eng. Taghi M. Khoshgoftaar, Edward B. Allen
2001	—	`conf`	Controlling Overfitting in Software Quality Models: Experiments with Regression Trees and Classification. IEEE METRICS Taghi M. Khoshgoftaar, Edward B. Allen, Jianyu Deng
2001	J	`jnl`	Cost-Benefit Analysis of Software Quality Models. Softw. Qual. J. Taghi M. Khoshgoftaar, Edward B. Allen, Wendell D. Jones, John P. Hudepohl
2001	J	`jnl`	Data Mining of Software Development Databases. Softw. Qual. J. Taghi M. Khoshgoftaar, Edward B. Allen, Wendell D. Jones, John P. Hudepohl
2001	J	`jnl`	Empirical Assessment of a Software Metric: The Information Content of Operators. Softw. Qual. J. Taghi M. Khoshgoftaar, Edward B. Allen
2001	—	`conf`	Genetic Programming Model for Software Quality Classification. HASE Yi Liu, Taghi M. Khoshgoftaar
2001	—	`conf`	Measuring Coupling and Cohesion of Software Modules: An Information-Theory Approach. IEEE METRICS Edward B. Allen, Taghi M. Khoshgoftaar, Ye Chen
2001	J	`jnl`	Software Quality Prediction for High-Assurance Network Telecommunications Systems. Comput. J. Zhiwei Xu, Taghi M. Khoshgoftaar
2000	J	`jnl`	A practical classification-rule for software-quality models. IEEE Trans. Reliab. Taghi M. Khoshgoftaar, Edward B. Allen
2000	J	`jnl`	Accuracy of software quality models over multiple releases. Ann. Softw. Eng. Taghi M. Khoshgoftaar, Edward B. Allen, Wendell D. Jones, John P. Hudepohl
2000	J	`jnl`	Balancing Misclassification Rates in Classification-Tree Models of Software Quality. Empir. Softw. Eng. Taghi M. Khoshgoftaar, Xiaojing Yuan, Edward B. Allen
2000	J	`jnl`	Case-Based Software Quality Prediction. Int. J. Softw. Eng. Knowl. Eng. K. Ganesan, Taghi M. Khoshgoftaar, Edward B. Allen
2000	J	`jnl`	Classification-tree models of software-quality over multiple releases. IEEE Trans. Reliab. Taghi M. Khoshgoftaar, Edward B. Allen, Wendell D. Jones, John P. Hudepohl
2000	A	`conf`	Improving Tree-Based Models of Software Quality with Principal Components Analysis. ISSRE Taghi M. Khoshgoftaar, Ruqun Shan, Edward B. Allen
2000	A	`conf`	Modeling Fault-Prone Modules of Subsystems. ISSRE Taghi M. Khoshgoftaar, Vishal Thaker, Edward B. Allen
2000	B	`conf`	Modeling software quality: the Software Measurement Analysis and Reliability Toolkit. ICTAI Taghi M. Khoshgoftaar, Edward B. Allen, Jason C. Busboom
2000	—	`conf`	Prediction of software faults using fuzzy nonlinear regression modeling. HASE Zhiwei Xu, Taghi M. Khoshgoftaar, Edward B. Allen
2000	—	`conf`	Using product, process, and execution metrics to predict fault-prone software modules with classification trees. HASE Taghi M. Khoshgoftaar, Ruqun Shan, Edward B. Allen
1999	J	`jnl`	A Comparative Study of Ordering and Classification of Fault-Prone Software Modules. Empir. Softw. Eng. Taghi M. Khoshgoftaar, Edward B. Allen
1999	—	`conf`	Application of a Usage Profile in Software Quality Models. CSMR Wendell D. Jones, John P. Hudepohl, Taghi M. Khoshgoftaar, Edward B. Allen
1999	—	`conf`	Assessing Uncertain Predictions of Software Quality. IEEE METRICS Taghi M. Khoshgoftaar, Edward B. Allen, Xiaojing Yuan, Wendell D. Jones, John P. Hudepohl
1999	—	`conf`	Can Metrics and Models be Applied Across Multiple Releases or Projects? IEEE METRICS Norman F. Schneidewind, Wendell D. Jones, Taghi M. Khoshgoftaar, Paul W. Oman, George E. Stark
1999	A	`conf`	Classification tree models of software quality over multiple releases. ISSRE Taghi M. Khoshgoftaar, Edward B. Allen, Wendell D. Jones, John P. Hudepohl
1999	J	`jnl`	Data Mining for Predictors of Software Quality. Int. J. Softw. Eng. Knowl. Eng. Taghi M. Khoshgoftaar, Edward B. Allen, Wendell D. Jones, John P. Hudepohl
1999	—	`conf`	Experience Paper: Preparing Measurements of Legacy Software for Predicting Operational Faults. ICSM Taghi M. Khoshgoftaar, Edward B. Allen, Xiaojing Yuan, Wendell D. Jones, John P. Hudepohl
1999	—	`conf`	Measuring Coupling and Cohesion: An Information-Theory Approach. IEEE METRICS Edward B. Allen, Taghi M. Khoshgoftaar
1999	A	`conf`	Modelling software quality with GP. GECCO Matthew P. Evett, Taghi M. Khoshgoftaar, Pei-der Chien, Edward B. Allen
1999	—	`conf`	Predicting Fault-Prone Software Modules in Embedded Systems with Classification Trees. HASE Taghi M. Khoshgoftaar, Edward B. Allen
1999	J	`jnl`	Using Classification Trees for Software Quality Models: Lessons Learned. Int. J. Softw. Eng. Knowl. Eng. Taghi M. Khoshgoftaar, Edward B. Allen, Archana Naik, Wendell D. Jones, John P. Hudepohl
1999	Misc	`conf`	Using Genetic Programming to Determine Software Quality. FLAIRS Matthew P. Evett, Taghi M. Khoshgoftaar, Pei-der Chien, Edward B. Allen
1999	J	`jnl`	Which Software Modules have Faults which will be Discovered by Customers? J. Softw. Maintenance Res. Pract. Taghi M. Khoshgoftaar, Edward B. Allen, Wendell D. Jones, John P. Hudepohl
1998	—	`conf`	Can a Software Quality Model Hit a Moving Target? ICSM Taghi M. Khoshgoftaar, Edward B. Allen
1998	J	`jnl`	Classification of Fault-Prone Software Modules: Prior Probabilities, Costs, and Model Evaluation. Empir. Softw. Eng. Taghi M. Khoshgoftaar, Edward B. Allen
1998	—	`conf`	Hitting the Moving Target: Trials and Tribulations of Modeling Quality in Evolving Software Systems. ICSM Wendell D. Jones, Taghi M. Khoshgoftaar, John C. Munson, T. Troy Pearse, George E. Stark
1998	A	`conf`	Predicting the order of fault-prone modules in legacy software. ISSRE Taghi M. Khoshgoftaar, Edward B. Allen
1998	—	`conf`	The Application of Fuzzy Enhanced Case-Based Reasoning for Identifying Fault-Prone Modules. HASE Donald F. Schenker, Taghi M. Khoshgoftaar
1998	—	`conf`	Using Classification Trees for Software Quality Models: Lessons Learned. HASE Taghi M. Khoshgoftaar, Edward B. Allen, Archana Naik, Wendell D. Jones, John P. Hudepohl
1998	J	`jnl`	Using Process History to Predict Software Quality. Computer Taghi M. Khoshgoftaar, Edward B. Allen, Robert Halstead, Gary P. Trio, Ronald M. Flass
1997	J	`jnl`	An Information Theory-Based Approach to Quantifying the Contribution of a Software Metric. J. Syst. Softw. Taghi M. Khoshgoftaar, Edward B. Allen, David L. Lanning
1997	J	`jnl`	Application of neural networks to software quality modeling of a very large telecommunications system. IEEE Trans. Neural Networks Taghi M. Khoshgoftaar, Edward B. Allen, John P. Hudepohl, Stephen J. Aud
1997	A	`conf`	Evolutionary neural networks: a robust approach to software reliability problems. ISSRE Robert Hochman, Taghi M. Khoshgoftaar, Edward B. Allen, John P. Hudepohl
1997	A	`conf`	Predicting fault-prone modules with case-based reasoning. ISSRE Taghi M. Khoshgoftaar, K. Ganesan, Edward B. Allen, Fletcher D. Ross, Rama Munikoti, Nishith Goel, Amit Nandi
1997	—	`conf`	Process Measures for Predicting Software Quality. HASE Taghi M. Khoshgoftaar, Edward B. Allen, Robert Halstead, Gary P. Trio, Ronald M. Flass
1997	—	`conf`	The Impact of Costs of Misclassification on Software Quality Modeling. IEEE METRICS Taghi M. Khoshgoftaar, Edward B. Allen
1996	—	`conf`	A tree-based classification model for analysis of a military software system. HASE Taghi M. Khoshgoftaar, Edward B. Allen, Lofton A. Bullard, Robert Halstead, Gary P. Trio
1996	J	`jnl`	Analysis and differentiation of software system environments. Softw. Qual. J. Taghi M. Khoshgoftaar, David L. Lanning
1996	—	`conf`	Detection of Fault-Prone Software Modules During a Spiral Life Cycle. ICSM Taghi M. Khoshgoftaar, Edward B. Allen, Robert Halstead, Gary P. Trio
1996	A	`conf`	Detection of software modules with high debug code churn in a very large legacy system. ISSRE Taghi M. Khoshgoftaar, Edward B. Allen, Nishith Goel, Amit Nandi, John McMullan
1996	J	`jnl`	Early Quality Prediction: A Case Study in Telecommunications. IEEE Softw. Taghi M. Khoshgoftaar, Edward B. Allen, Kalai Kalaichelvan, Nishith Goel
1996	J	`jnl`	Emerald: Software Metrics and Models on the Desktop. IEEE Softw. John P. Hudepohl, Stephen J. Aud, Taghi M. Khoshgoftaar, Edward B. Allen, Jean Mayrand
1996	A	`conf`	Integrating metrics and models for software risk assessment. ISSRE John P. Hudepohl, Stephen J. Aud, Taghi M. Khoshgoftaar, Edward B. Allen, Jean Mayrand
1996	J	`jnl`	The impact of software evolution and reuse on software quality. Empir. Softw. Eng. Taghi M. Khoshgoftaar, Edward B. Allen, Kalai Kalaichelvan, Nishith Goel
1996	J	`jnl`	Using neural networks to predict software faults during testing. IEEE Trans. Reliab. Taghi M. Khoshgoftaar, Robert M. Szabo
1996	A	`conf`	Using the genetic algorithm to build optimal neural networks for fault-prone module detection. ISSRE Robert Hochman, Taghi M. Khoshgoftaar, Edward B. Allen, John P. Hudepohl
1995	J	`jnl`	A Performance Analysis of an Object-Based I/O Architecture in a Video Server Environment. Multim. Syst. Khoa D. Huynh, Taghi M. Khoshgoftaar
1995	J	`jnl`	A neural network approach for early detection of program modules having high risk in the maintenance phase. J. Syst. Softw. Taghi M. Khoshgoftaar, David L. Lanning
1995	A	`conf`	An assessment of software quality in a C++ environment. ISSRE Robert M. Szabo, Taghi M. Khoshgoftaar
1995	J	`jnl`	Application of Neural Networks for Predicting Program Faults. Ann. Softw. Eng. Taghi M. Khoshgoftaar, Abhijit S. Pandya, David L. Lanning
1995	—	`conf`	Detecting program modules with low testability. ICSM Taghi M. Khoshgoftaar, Robert M. Szabo, Jeffrey M. Voas
1995	A	`conf`	Detection of fault-prone program modules in a very large telecommunications system. ISSRE Taghi M. Khoshgoftaar, Edward B. Allen, Kalai Kalaichelvan, Nishith Goel, John P. Hudepohl, Jean Mayrand
1995	J	`jnl`	Exploring the behaviour of neural network software quality models. Softw. Eng. J. Taghi M. Khoshgoftaar, Robert M. Szabo, Peter J. Guasti
1995	J	`jnl`	Investigating ARIMA models of software system quality. Softw. Qual. J. Taghi M. Khoshgoftaar, Robert M. Szabo
1995	B	`conf`	Multivariate assessment of complex software systems: a comparative study. ICECCS Taghi M. Khoshgoftaar, Edward B. Allen
1995	J	`jnl`	Performance Analysis of a Peer-to-Peer I/O Architecture in Video Server Environments. Multim. Tools Appl. Khoa D. Huynh, Taghi M. Khoshgoftaar
1994	J	`jnl`	A Performance Analysis of Personal Computers in a Video Conferencing Environment. Multim. Syst. Khoa D. Huynh, Taghi M. Khoshgoftaar
1994	J	`jnl`	A comparative study of pattern recognition techniques for quality evaluation of telecommunications software. IEEE J. Sel. Areas Commun. Taghi M. Khoshgoftaar, David L. Lanning, Abhijit S. Pandya
1994	J	`jnl`	A performance analysis of advanced I/O architectures for PC-based network file servers. Distributed Syst. Eng. Khoa D. Huynh, Taghi M. Khoshgoftaar
1994	J	`jnl`	Alternative approaches for the use of metrics to order programs by complexity. J. Syst. Softw. Taghi M. Khoshgoftaar, John C. Munson, David L. Lanning
1994	—	`conf`	Are the principal components of software complexity data stable across software products? IEEE METRICS Taghi M. Khoshgoftaar, David L. Lanning
1994	—	`conf`	Canonical Modeling of Software Complexity and Fault Correction Activity. ICSM David L. Lanning, Taghi M. Khoshgoftaar
1994	—	`conf`	Improving Code Churn Predictions During the System Test and Maintenance Phases. ICSM Taghi M. Khoshgoftaar, Robert M. Szabo
1994	J	`jnl`	Modeling the Relationship Between Source Code Complexity and Maintenance Difficulty. Computer David L. Lanning, Taghi M. Khoshgoftaar
1994	A	`conf`	On the impact of software product dissimilarity on software quality models. ISSRE Taghi M. Khoshgoftaar, David L. Lanning
1994	J	`jnl`	Performance Analysis of Advanced I/O Architectures for PC-Based Video Servers. Multim. Syst. Khoa D. Huynh, Taghi M. Khoshgoftaar
1994	J	`jnl`	Software Metrics: Charting the Course - Guest Editors' Introduction. Computer Taghi M. Khoshgoftaar, Paul W. Oman
1993	—	`conf`	A Comparative Study of Predictive Models for Program Changes During System Testing and Maintenance. ICSM Taghi M. Khoshgoftaar, John C. Munson, David L. Lanning
1993	A*	`conf`	A Performance Analysis of the IBM Subsystem Control Block Architecture in a Video Conferencing Environment. ACM Multimedia Khoa D. Huynh, Taghi M. Khoshgoftaar
1993	J	`jnl`	A high-level performance analysis of the IBM subsystem control block (SCB) architecture. Microprocess. Microprogramming Khoa D. Huynh, Taghi M. Khoshgoftaar, Gerald Marazas
1993	A	`conf`	A neural network modeling methodology for the detection of high-risk programs. ISSRE Taghi M. Khoshgoftaar, David L. Lanning, Abhijit S. Pandya
1993	—	`conf`	Dynamic system complexity. IEEE METRICS Taghi M. Khoshgoftaar, John C. Munson, David L. Lanning
1993	J	`jnl`	Measurement of data structure complexity. J. Syst. Softw. John C. Munson, Taghi M. Khoshgoftaar
1992	A	`conf`	A neural network approach for predicting software development faults. ISSRE Taghi M. Khoshgoftaar, Abhijit S. Pandya, Hemant B. More
1992	J	`jnl`	A workload model for frame-based real-time applications on distributed systems. J. Syst. Softw. Khoa D. Huynh, Eduardo B. Fernández, Taghi M. Khoshgoftaar
1992	J	`jnl`	Measuring Dynamic Program Complexity. IEEE Softw. John C. Munson, Taghi M. Khoshgoftaar
1992	J	`jnl`	Predictive Modeling Techniques of Software Quality from Software Measures. IEEE Trans. Software Eng. Taghi M. Khoshgoftaar, John C. Munson, Bibhuti B. Bhattacharya, Gary D. Richardson
1992	—	`conf`	Software measurement for the space shuttle HAL/S maintenance environment. ICSM John C. Munson, Taghi M. Khoshgoftaar
1992	J	`jnl`	The Detection of Fault-Prone Programs. IEEE Trans. Software Eng. John C. Munson, Taghi M. Khoshgoftaar
1991	A	`conf`	Software reliability model selection: a cast study. ISSRE Taghi M. Khoshgoftaar, Timothy G. Woodcock
1991	A	`conf`	The use of software complexity metrics in software reliability modeling. ISSRE John C. Munson, Taghi M. Khoshgoftaar
1990	J	`jnl`	Applications of a relative complexity metric for software project management. J. Syst. Softw. John C. Munson, Taghi M. Khoshgoftaar
1990	J	`jnl`	Predicting Software Development Errors Using Software Complexity Metrics. IEEE J. Sel. Areas Commun. Taghi M. Khoshgoftaar, John C. Munson
1990	B	`conf`	The lines of code metric as a predictor of program faults: a critical analysis. COMPSAC Taghi M. Khoshgoftaar, John C. Munson
1989	A*	`conf`	The Dimensionality of Program Complexity. ICSE John C. Munson, Taghi M. Khoshgoftaar