Kai Ming Ting - RankMe

194 papers A* 34A 3B 11C 1Misc 2Journal 97Unranked 36

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	A New Framework for Explainable Rare Cell Identification in Single-Cell Transcriptomics Data. CoRR Di Su, Kai Ming Ting, Jie Zhang, Xiaorui Zhang, Xinpeng Li
2026	A*	`conf`	Distribution-Based Feature Attribution for Explaining the Predictions of Any Classifier. AAAI Xinpeng Li, Kai Ming Ting
2026	A*	`conf`	GeoPTH: A Lightweight Approach to Category-Based Trajectory Retrieval via Geometric Prototype Trajectory Hashing. AAAI Yang Xu, Zuliang Yang, Kai Ming Ting
2026	J	`jnl`	How to Achieve the Intended Aim of Deep Clustering Now, without Deep Learning. CoRR Kai Ming Ting, Wei-Jie Xu, Hang Zhang
2026	A*	`conf`	IDK-S: Incremental Distributional Kernel for Streaming Anomaly Detection. AAAI Yang Xu, Yixiao Ma, Kaifeng Zhang, Zuliang Yang, Kai Ming Ting
2026	J	`jnl`	Interpretable Graph-Level Anomaly Detection via Contrast with Normal Prototypes. CoRR Qiuran Zhao, Kai Ming Ting, Xinpeng Li
2026	J	`jnl`	Kernel-bounded clustering: Achieving the objective of spectral clustering without eigendecomposition. Artif. Intell. Hang Zhang, Kai Ming Ting, Ye Zhu
2026	J	`jnl`	LLMs Meet Isolation Kernel: Lightweight, Learning-free Binary Embeddings for Fast Retrieval. CoRR Zhibo Zhang, Yang Xu, Kai Ming Ting, Cam-Tu Nguyen
2026	J	`jnl`	Mass Distribution versus Density Distribution in the Context of Clustering. CoRR Kai Ming Ting, Ye Zhu, Hang Zhang, Tianrun Liang
2026	J	`jnl`	Optimization-Free Graph Embedding via Distributional Kernel for Community Detection. CoRR Shuaibin Song, Kai Ming Ting, Kaifeng Zhang, Tianrun Liang
2026	J	`jnl`	Rethinking Divisive Hierarchical Clustering from a Distributional Perspective. CoRR Kaifeng Zhang, Kai Ming Ting, Tianrun Liang, Qiuran Zhao
2026	A*	`conf`	SCoNE: Spherical Consistent Neighborhoods Ensemble for Effective and Efficient Multi-View Anomaly Detection. AAAI Yang Xu, Hang Zhang, Yixiao Ma, Ye Zhu, Kai Ming Ting
2025	J	`jnl`	A new filter for deformation-invariant persistence diagram. Mach. Learn. Kaifeng Zhang, Hang Zhang, Kai Ming Ting, Tianrun Liang
2025	J	`jnl`	Anomaly Detection Based on Isolation Mechanisms: A Survey. Mach. Intell. Res. Yang Cao, Haolong Xiang, Hang Zhang, Ye Zhu, Kai Ming Ting
2025	A	`conf`	Contrastive Multi-View Graph Hashing. CIKM Yang Xu, Zuliang Yang, Kai Ming Ting
2025	J	`jnl`	Contrastive Multi-View Graph Hashing. CoRR Yang Xu, Zuliang Yang, Kai Ming Ting
2025	J	`jnl`	Distribution-Based Feature Attribution for Explaining the Predictions of Any Classifier. CoRR Xinpeng Li, Kai Ming Ting
2025	J	`jnl`	GeoPTH: A Lightweight Approach to Category-Based Trajectory Retrieval via Geometric Prototype Trajectory Hashing. CoRR Yang Xu, Zuliang Yang, Kai Ming Ting
2025	J	`jnl`	IDK-S: Incremental Distributional Kernel for Streaming Anomaly Detection. CoRR Yang Xu, Yixiao Ma, Kaifeng Zhang, Zuliang Yang, Kai Ming Ting
2025	—	`conf`	Machine Unlearning for Random Forest via Method of Images. ECML/PKDD (5) Hang Zhang, Kai Ming Ting
2025	J	`jnl`	Revisiting streaming anomaly detection: benchmark and evaluation. Artif. Intell. Rev. Yang Cao, Yixiao Ma, Ye Zhu, Kai Ming Ting
2025	J	`jnl`	SCoNE: Spherical Consistent Neighborhoods Ensemble for Effective and Efficient Multi-View Anomaly Detection. CoRR Yang Xu, Hang Zhang, Yixiao Ma, Ye Zhu, Kai Ming Ting
2025	—	`conf`	Streaming Hierarchical Clustering for Emerging New Class. KSEM (1) Yixiao Ma, Ye Zhu, Yang Xu, Kai Ming Ting
2025	J	`jnl`	Towards a Robust Persistence Diagram via Data-dependent Kernel. J. Artif. Intell. Res. Hang Zhang, Kaifeng Zhang, Kai Ming Ting, Ye Zhu
2025	—	`conf`	Voronoi Diagram Encoded Hashing. ECML/PKDD (2) Yang Xu, Kai Ming Ting
2025	J	`jnl`	Voronoi Diagram Encoded Hashing. CoRR Yang Xu, Kai Ming Ting
2025	J	`jnl`	What Are Anomalies in a Network? ACM Trans. Knowl. Discov. Data Kai Ming Ting, Zhong Zhuang, Guansong Pang, Zongyou Liu, Tianrun Liang, Qiuran Zhao
2024	J	`jnl`	A Principled Distributional Approach to Trajectory Similarity Measurement and its Application to Anomaly Detection. J. Artif. Intell. Res. Yufan Wang, Zijing Wang, Kai Ming Ting, Yuanyi Shang
2024	J	`jnl`	A new distributional treatment for time series anomaly detection. VLDB J. Kai Ming Ting, Zongyou Liu, Lei Gong, Hang Zhang, Ye Zhu
2024	J	`jnl`	An Online Automatic Modulation Classification Scheme Based on Isolation Distributional Kernel. CoRR Xinpeng Li, Zile Jiang, Kai Ming Ting, Ye Zhu
2024	J	`jnl`	Anomaly Detection Based on Isolation Mechanisms: A Survey. CoRR Yang Cao, Haolong Xiang, Hang Zhang, Ye Zhu, Kai Ming Ting
2024	J	`jnl`	Detecting Change Intervals with Isolation Distributional Kernel. J. Artif. Intell. Res. Yang Cao, Ye Zhu, Kai Ming Ting, Flora D. Salim, Hong Xian Li, Luxing Yang, Gang Li
2024	A*	`conf`	Detecting Change Intervalswith Isolation Distributional Kernel (Abstract Reprint). IJCAI Yang Cao, Ye Zhu, Kai Ming Ting, Flora D. Salim, Hong Xian Li, Luxing Yang, Gang Li
2024	J	`jnl`	Distributed Clustering based on Distributional Kernel. CoRR Hang Zhang, Yang Xu, Lei Gong, Ye Zhu, Kai Ming Ting
2024	—	`conf`	Distributional Kernel: An Effective and Efficient Means for Trajectory Retrieval. PAKDD (5) Yuanyi Shang, Kai Ming Ting, Zijing Wang, Yufan Wang
2024	J	`jnl`	Is it possible to find the single nearest neighbor of a query in high dimensions? Artif. Intell. Kai Ming Ting, Takashi Washio, Ye Zhu, Yang Xu, Kaifeng Zhang
2024	—	`conf`	Local Subsequence-Based Distribution for Time Series Clustering. PAKDD (1) Lei Gong, Hang Zhang, Zongyou Liu, Kai Ming Ting, Yang Cao, Ye Zhu
2024	J	`jnl`	RAD: A Dataset and Benchmark for Real-Life Anomaly Detection with Robotic Observations. CoRR Kaichen Zhou, Yang Cao, Taewhan Kim, Hao Zhao, Hao Dong, Kai Ming Ting, Ye Zhu
2023	J	`jnl`	A principled distributional approach to trajectory similarity measurement. CoRR Yufan Wang, Kai Ming Ting, Yuanyi Shang
2023	A*	`conf`	Distribution-Based Trajectory Clustering. ICDM Zijing Wang, Ye Zhu, Kai Ming Ting
2023	J	`jnl`	Distribution-Based Trajectory Clustering. CoRR Zijing Wang, Ye Zhu, Kai Ming Ting
2023	J	`jnl`	Isolation Distributional Kernel: A New Tool for Point and Group Anomaly Detections. IEEE Trans. Knowl. Data Eng. Kai Ming Ting, Bi-Cun Xu, Takashi Washio, Zhi-Hua Zhou
2023	J	`jnl`	Isolation Kernel Estimators. Knowl. Inf. Syst. Kai Ming Ting, Takashi Washio, Jonathan R. Wells, Hang Zhang, Ye Zhu
2023	J	`jnl`	Kernel-based clustering via Isolation Distributional Kernel. Inf. Syst. Ye Zhu, Kai Ming Ting
2023	J	`jnl`	Point-Set Kernel Clustering. IEEE Trans. Knowl. Data Eng. Kai Ming Ting, Jonathan R. Wells, Ye Zhu
2023	A	`conf`	Subgraph Centralization: A Necessary Step for Graph Anomaly Detection. SDM Zhong Zhuang, Kai Ming Ting, Guansong Pang, Shuaibin Song
2023	J	`jnl`	Subgraph Centralization: A Necessary Step for Graph Anomaly Detection. CoRR Zhong Zhuang, Kai Ming Ting, Guansong Pang, Shuaibin Song
2023	J	`jnl`	The impact of isolation kernel on agglomerative hierarchical clustering algorithms. Pattern Recognit. Xin Han, Ye Zhu, Kai Ming Ting, Gang Li
2023	A*	`conf`	Towards a Persistence Diagram that is Robust to Noise and Varied Densities. ICML Hang Zhang, Kaifeng Zhang, Kai Ming Ting, Ye Zhu
2022	J	`jnl`	A New Distributional Treatment for Time Series and An Anomaly Detection Investigation. Proc. VLDB Endow. Kai Ming Ting, Zongyou Liu, Hang Zhang, Ye Zhu
2022	J	`jnl`	Detecting Change Intervals with Isolation Distributional Kernel. CoRR Yang Cao, Ye Zhu, Kai Ming Ting, Flora D. Salim, Hong Xian Li, Gang Li
2022	J	`jnl`	Hierarchical clustering that takes advantage of both density-peak and density-connectivity. Inf. Syst. Ye Zhu, Kai Ming Ting, Yuan Jin, Maia Angelova
2022	J	`jnl`	Improving Deep Forest by Screening. IEEE Trans. Knowl. Data Eng. Ming Pang, Kai Ming Ting, Peng Zhao, Zhi-Hua Zhou
2022	A*	`conf`	Improving the Effectiveness and Efficiency of Stochastic Neighbour Embedding with Isolation Kernel (Extended Abstract). IJCAI Ye Zhu, Kai Ming Ting
2022	J	`jnl`	Spectral-Spatial Anomaly Detection of Hyperspectral Data Based on Improved Isolation Forest. IEEE Trans. Geosci. Remote. Sens. Xiangyu Song, Sunil Aryal, Kai Ming Ting, Zhen Liu, Bin He
2022	A*	`conf`	Streaming Hierarchical Clustering Based on Point-Set Kernel. KDD Xin Han, Ye Zhu, Kai Ming Ting, De-Chuan Zhan, Gang Li
2021	J	`jnl`	Breaking the curse of dimensionality with Isolation Kernel. CoRR Kai Ming Ting, Takashi Washio, Ye Zhu, Yang Xu
2021	J	`jnl`	CDF Transform-and-Shift: An effective way to deal with datasets of inhomogeneous cluster densities. Pattern Recognit. Ye Zhu, Kai Ming Ting, Mark J. Carman, Maia Angelova
2021	J	`jnl`	Improving the Effectiveness and Efficiency of Stochastic Neighbour Embedding with Isolation Kernel. J. Artif. Intell. Res. Ye Zhu, Kai Ming Ting
2021	A*	`conf`	Isolation Graph Kernel. AAAI Bi-Cun Xu, Kai Ming Ting, Yuan Jiang
2021	A*	`conf`	Isolation Kernel Density Estimation. ICDM Kai Ming Ting, Takashi Washio, Jonathan R. Wells, Hang Zhang
2021	J	`jnl`	Isolation kernel: the X factor in efficient and effective large scale online kernel learning. Data Min. Knowl. Discov. Kai Ming Ting, Jonathan R. Wells, Takashi Washio
2021	A	`conf`	Reconstruction-based Anomaly Detection with Completely Random Forest. SDM Yi-Xuan Xu, Ming Pang, Ji Feng, Kai Ming Ting, Yuan Jiang, Zhi-Hua Zhou
2020	—	`conf`	A New Effective and Efficient Measure for Outlying Aspect Mining. WISE (2) Durgesh Samariya, Sunil Aryal, Kai Ming Ting, Jiangang Ma
2020	J	`jnl`	A comparative study of data-dependent approaches without learning in measuring similarities of data objects. Data Min. Knowl. Discov. Sunil Aryal, Kai Ming Ting, Takashi Washio, Gholamreza Haffari
2020	—	`conf`	Anomaly Detection via Neighbourhood Contrast. PAKDD (2) Bo Chen, Kai Ming Ting, Tat-Jun Chin
2020	J	`jnl`	Clustering based on Point-Set Kernel. CoRR Kai Ming Ting, Jonathan R. Wells, Ye Zhu
2020	A*	`conf`	Isolation Distributional Kernel: A New Tool for Kernel based Anomaly Detection. KDD Kai Ming Ting, Bi-Cun Xu, Takashi Washio, Zhi-Hua Zhou
2020	J	`jnl`	Isolation Distributional Kernel: A New Tool for Point & Group Anomaly Detection. CoRR Kai Ming Ting, Bi-Cun Xu, Takashi Washio, Zhi-Hua Zhou
2020	J	`jnl`	Simple supervised dissimilarity measure: Bolstering iForest-induced similarity with class information without learning. Knowl. Inf. Syst. Jonathan R. Wells, Sunil Aryal, Kai Ming Ting
2020	J	`jnl`	The Impact of Isolation Kernel on Agglomerative Hierarchical Clustering Algorithms. CoRR Xin Han, Ye Zhu, Kai Ming Ting, Gang Li
2019	J	`jnl`	A new simple and effective measure for bag-of-word inter-document similarity measurement. CoRR Sunil Aryal, Kai Ming Ting, Takashi Washio, Gholamreza Haffari
2019	J	`jnl`	A new simple and efficient density estimator that enables fast systematic search. Pattern Recognit. Lett. Jonathan R. Wells, Kai Ming Ting
2019	J	`jnl`	Improving Stochastic Neighbour Embedding fundamentally with a well-defined data-dependent kernel. CoRR Ye Zhu, Kai Ming Ting
2019	J	`jnl`	Isolation Kernel: The X Factor in Efficient and Effective Large Scale Online Kernel Learning. CoRR Kai Ming Ting, Jonathan R. Wells, Takashi Washio
2019	A*	`conf`	Isolation Set-Kernel and Its Application to Multi-Instance Learning. KDD Bi-Cun Xu, Kai Ming Ting, Zhi-Hua Zhou
2019	J	`jnl`	Lowest probability mass neighbour algorithms: relaxing the metric constraint in distance-based neighbourhood algorithms. Mach. Learn. Kai Ming Ting, Ye Zhu, Mark J. Carman, Yue Zhu, Takashi Washio, Zhi-Hua Zhou
2019	A*	`conf`	Nearest-Neighbour-Induced Isolation Similarity and Its Impact on Density-Based Clustering. AAAI Xiaoyu Qin, Kai Ming Ting, Ye Zhu, Vincent C. S. Lee
2019	J	`jnl`	Nearest-Neighbour-Induced Isolation Similarity and its Impact on Density-Based Clustering. CoRR Xiaoyu Qin, Kai Ming Ting, Ye Zhu, Vincent C. S. Lee
2018	—	`conf`	A Distance Scaling Method to Improve Density-Based Clustering. PAKDD (3) Ye Zhu, Kai Ming Ting, Maia Angelova
2018	J	`jnl`	CDF Transform-Shift: An effective way to deal with inhomogeneous density datasets. CoRR Ye Zhu, Kai Ming Ting, Mark J. Carman, Maia Angelova
2018	J	`jnl`	Data Dependent Dissimilarity Measures (NII Shonan Meeting 2018-13). NII Shonan Meet. Rep. Kai Ming Ting, Takashi Washio, Ata Kabán
2018	J	`jnl`	Grouping points by shared subspaces for effective subspace clustering. Pattern Recognit. Ye Zhu, Kai Ming Ting, Mark J. Carman
2018	J	`jnl`	Hierarchical clustering that takes advantage of both density-peak and density-connectivity. CoRR Ye Zhu, Kai Ming Ting, Yuan Jin, Maia Angelova
2018	A*	`conf`	Isolation Kernel and Its Effect on SVM. KDD Kai Ming Ting, Yue Zhu, Zhi-Hua Zhou
2018	J	`jnl`	Isolation-based anomaly detection using nearest-neighbor ensembles. Comput. Intell. Tharindu R. Bandaragoda, Kai Ming Ting, David W. Albrecht, Fei Tony Liu, Ye Zhu, Jonathan R. Wells
2018	J	`jnl`	Local contrast as an effective means to robust clustering against varying densities. Mach. Learn. Bo Chen, Kai Ming Ting, Takashi Washio, Ye Zhu
2018	J	`jnl`	Multi-Label Learning with Emerging New Labels. IEEE Trans. Knowl. Data Eng. Yue Zhu, Kai Ming Ting, Zhi-Hua Zhou
2018	—	`conf`	Neighbourhood Contrast: A Better Means to Detect Clusters Than Density. PAKDD (3) Bo Chen, Kai Ming Ting
2018	A*	`conf`	Which Outlier Detector Should I use? ICDM Kai Ming Ting, Sunil Aryal, Takashi Washio
2017	J	`jnl`	A simple efficient density estimator that enables fast systematic search. CoRR Jonathan R. Wells, Kai Ming Ting
2017	J	`jnl`	Classification Under Streaming Emerging New Classes: A Solution Using Completely-Random Trees. IEEE Trans. Knowl. Data Eng. Xin Mu, Kai Ming Ting, Zhi-Hua Zhou
2017	—	`ch.`	Confusion Matrix. Encyclopedia of Machine Learning and Data Mining Kai Ming Ting
2017	J	`jnl`	Data-dependent dissimilarity measure: an effective alternative to geometric distance measures. Knowl. Inf. Syst. Sunil Aryal, Kai Ming Ting, Takashi Washio, Gholamreza Haffari
2017	J	`jnl`	Defying the gravity of learning curve: a characteristic of nearest neighbour anomaly detectors. Mach. Learn. Kai Ming Ting, Takashi Washio, Jonathan R. Wells, Sunil Aryal
2017	A*	`conf`	Discover Multiple Novel Labels in Multi-Instance Multi-Label Learning. AAAI Yue Zhu, Kai Ming Ting, Zhi-Hua Zhou
2017	—	`ch.`	Error Rate. Encyclopedia of Machine Learning and Data Mining Kai Ming Ting
2017	A*	`conf`	New Class Adaptation Via Instance Generation in One-Pass Class Incremental Learning. ICDM Yue Zhu, Kai Ming Ting, Zhi-Hua Zhou
2017	—	`ch.`	Precision and Recall. Encyclopedia of Machine Learning and Data Mining Kai Ming Ting
2017	—	`ch.`	Precision. Encyclopedia of Machine Learning and Data Mining Kai Ming Ting
2017	—	`ch.`	Sensitivity and Specificity. Encyclopedia of Machine Learning and Data Mining Kai Ming Ting
2016	J	`jnl`	A Generic Ensemble Approach to Estimate Multidimensional Likelihood in Bayesian Classifier Learning. Comput. Intell. Sunil Aryal, Kai Ming Ting
2016	J	`jnl`	Classification under Streaming Emerging New Classes: A Solution using Completely Random Trees. CoRR Xin Mu, Kai Ming Ting, Zhi-Hua Zhou
2016	J	`jnl`	Commentary: a decomposition of the outlier detection problem into a set of supervised learning problems. Mach. Learn. Ye Zhu, Kai Ming Ting
2016	J	`jnl`	Density-ratio based clustering for discovering clusters with varying densities. Pattern Recognit. Ye Zhu, Kai Ming Ting, Mark James Carman
2016	A*	`conf`	Multi-label Learning with Emerging New Labels. ICDM Yue Zhu, Kai Ming Ting, Zhi-Hua Zhou
2016	A*	`conf`	Overcoming Key Weaknesses of Distance-based Neighbourhood Methods using a Data Dependent Dissimilarity Measure. KDD Kai Ming Ting, Ye Zhu, Mark James Carman, Yue Zhu, Zhi-Hua Zhou
2016	—	`conf`	Revisiting Attribute Independence Assumption in Probabilistic Unsupervised Anomaly Detection. PAISI Sunil Aryal, Kai Ming Ting, Gholamreza Haffari
2016	J	`jnl`	ZERO++: Harnessing the Power of Zero Appearances to Detect Anomalies in Large-Scale Data Sets. J. Artif. Intell. Res. Guansong Pang, Kai Ming Ting, David W. Albrecht, Huidong Jin
2015	—	`conf`	Beyond tf-idf and Cosine Distance in Documents Dissimilarity Measure. AIRS Sunil Aryal, Kai Ming Ting, Gholamreza Haffari, Takashi Washio
2015	J	`jnl`	Half-space mass: a maximally robust and efficient data depth method. Mach. Learn. Bo Chen, Kai Ming Ting, Takashi Washio, Gholamreza Haffari
2015	—	`conf`	LeSiNN: Detecting Anomalies by Identifying Least Similar Nearest Neighbours. ICDM Workshops Guansong Pang, Kai Ming Ting, David W. Albrecht
2014	—	`conf`	Efficient Anomaly Detection by Isolation Using Nearest Neighbour Ensemble. ICDM Workshops Tharindu R. Bandaragoda, Kai Ming Ting, David W. Albrecht, Fei Tony Liu, Jonathan R. Wells
2014	—	`conf`	Improving iForest with Relative Mass. PAKDD (2) Sunil Aryal, Kai Ming Ting, Jonathan R. Wells, Takashi Washio
2014	J	`jnl`	LiNearN: A new approach to nearest neighbour density estimator. Pattern Recognit. Jonathan R. Wells, Kai Ming Ting, Takashi Washio
2014	A*	`conf`	Mp-Dissimilarity: A Data Dependent Dissimilarity Measure. ICDM Sunil Aryal, Kai Ming Ting, Gholamreza Haffari, Takashi Washio
2013	J	`jnl`	DEMass: a new density estimator for big data. Knowl. Inf. Syst. Kai Ming Ting, Takashi Washio, Jonathan R. Wells, Fei Tony Liu, Sunil Aryal
2013	J	`jnl`	Efficient nonlinear classification via low-rank regularised least squares. Neural Comput. Appl. Zhouyu Fu, Guojun Lu, Kai Ming Ting, Dengsheng Zhang
2013	J	`jnl`	Learning Sparse Kernel Classifiers for Multi-Instance Classification. IEEE Trans. Neural Networks Learn. Syst. Zhouyu Fu, Guojun Lu, Kai Ming Ting, Dengsheng Zhang
2013	J	`jnl`	Local Models - the Key to Boosting Stable Learners Successfully. Comput. Intell. Kai Ming Ting, Lian Zhu, Jonathan R. Wells
2013	J	`jnl`	Mass estimation. Mach. Learn. Kai Ming Ting, Guang-Tong Zhou, Fei Tony Liu, Swee Chuan Tan
2013	—	`conf`	MassBayes: A New Generative Classifier with Multi-dimensional Likelihood Estimation. PAKDD (1) Sunil Aryal, Kai Ming Ting
2013	A*	`conf`	Optimizing Cepstral Features for Audio Classification. IJCAI Zhouyu Fu, Guojun Lu, Kai Ming Ting, Dengsheng Zhang
2012	Misc	`conf`	A non-time series approach to vehicle related time series problems. AusDM Jonathan R. Wells, Kai Ming Ting, Naiwala P. Chandrasiri
2012	J	`jnl`	Isolation-Based Anomaly Detection. ACM Trans. Knowl. Discov. Data Fei Tony Liu, Kai Ming Ting, Zhi-Hua Zhou
2012	—	`conf`	Learning Sparse Kernel Classifiers in the Primal. SSPR/SPR Zhouyu Fu, Guojun Lu, Kai Ming Ting, Dengsheng Zhang
2012	J	`jnl`	Learning by extrapolation from marginal to full-multivariate probability distributions: decreasingly naive Bayesian classification. Mach. Learn. Geoffrey I. Webb, Janice R. Boughton, Fei Zheng, Kai Ming Ting, Houssam Salem
2012	J	`jnl`	Relevance feature mapping for content-based multimedia information retrieval. Pattern Recognit. Guang-Tong Zhou, Kai Ming Ting, Fei Tony Liu, Yilong Yin
2011	J	`jnl`	A Survey of Audio-Based Music Classification and Annotation. IEEE Trans. Multim. Zhouyu Fu, Guojun Lu, Kai Ming Ting, Dengsheng Zhang
2011	J	`jnl`	A general stochastic clustering method for automatic cluster discovery. Pattern Recognit. Swee Chuan Tan, Kai Ming Ting, Shyh Wei Teng
2011	—	`conf`	Building Sparse Support Vector Machines for Multi-Instance Classification. ECML/PKDD (1) Zhouyu Fu, Guojun Lu, Kai Ming Ting, Dengsheng Zhang
2011	A*	`conf`	Density Estimation Based on Mass. ICDM Kai Ming Ting, Takashi Washio, Jonathan R. Wells, Fei Tony Liu
2011	A*	`conf`	Fast Anomaly Detection for Streaming Data. IJCAI Swee Chuan Tan, Kai Ming Ting, Fei Tony Liu
2011	J	`jnl`	Feature-subspace aggregating: ensembles for stable and unstable learners. Mach. Learn. Kai Ming Ting, Jonathan R. Wells, Swee Chuan Tan, Shyh Wei Teng, Geoffrey I. Webb
2011	J	`jnl`	Issues in Stacked Generalization CoRR Kai Ming Ting, Ian H. Witten
2011	J	`jnl`	Music classification via the bag-of-features approach. Pattern Recognit. Lett. Zhouyu Fu, Guojun Lu, Kai Ming Ting, Dengsheng Zhang
2011	—	`conf`	On Low-Rank Regularized Least Squares for Scalable Nonlinear Classification. ICONIP (2) Zhouyu Fu, Guojun Lu, Kai Ming Ting, Dengsheng Zhang
2011	Misc	`conf`	Simplifying and improving ant-based clustering. ICCS Swee Chuan Tan, Kai Ming Ting, Shyh Wei Teng
2010	—	`conf`	A Comparative Study of a Practical Stochastic Clustering Method with Traditional Methods. Australasian Conference on Artificial Intelligence Swee Chuan Tan, Kai Ming Ting, Shyh Wei Teng
2010	J	`jnl`	Best papers from the 12th Pacific-Asia conference on knowledge discovery and data mining (PAKDD2008). Knowl. Inf. Syst. Takashi Washio, Einoshin Suzuki, Kai Ming Ting
2010	—	`ch.`	Confusion Matrix. Encyclopedia of Machine Learning Kai Ming Ting
2010	—	`ch.`	Error Rate. Encyclopedia of Machine Learning Kai Ming Ting
2010	B	`conf`	Learning Naive Bayes Classifiers for Music Classification and Retrieval. ICPR Zhouyu Fu, Guojun Lu, Kai Ming Ting, Dengsheng Zhang
2010	A*	`conf`	Mass estimation and its applications. KDD Kai Ming Ting, Guang-Tong Zhou, Fei Tony Liu, James Swee Chuan Tan
2010	A*	`conf`	Multi-dimensional Mass Estimation and Mass-based Clustering. ICDM Kai Ming Ting, Jonathan R. Wells
2010	—	`conf`	On Detecting Clustered Anomalies Using SCiForest. ECML/PKDD (2) Fei Tony Liu, Kai Ming Ting, Zhi-Hua Zhou
2010	—	`conf`	On Feature Combination for Music Classification. SSPR/SPR Zhouyu Fu, Guojun Lu, Kai Ming Ting, Dengsheng Zhang
2010	—	`ch.`	Precision and Recall. Encyclopedia of Machine Learning Kai Ming Ting
2010	—	`ch.`	Precision. Encyclopedia of Machine Learning Kai Ming Ting
2010	—	`ch.`	Sensitivity and Specificity. Encyclopedia of Machine Learning Kai Ming Ting
2009	—	`conf`	Boosting Support Vector Machines Successfully. MCS Kai Ming Ting, Lian Zhu
2009	—	`conf`	FaSS: Ensembles for Stable Learners. MCS Kai Ming Ting, Jonathan R. Wells, Swee Chuan Tan, Shyh Wei Teng, Geoffrey I. Webb
2008	B	`ed.`	Advances in Knowledge Discovery and Data Mining, 12th Pacific-Asia Conference, PAKDD 2008, Osaka, Japan, May 20-23, 2008 Proceedings PAKDD Takashi Washio, Einoshin Suzuki, Kai Ming Ting, Akihiro Inokuchi
2008	A*	`conf`	Isolation Forest. ICDM Fei Tony Liu, Kai Ming Ting, Zhi-Hua Zhou
2008	B	`conf`	Issues of grid-cluster retrievals in swarm-based clustering. IEEE Congress on Evolutionary Computation Swee Chuan Tan, Kai Ming Ting, Shyh Wei Teng
2008	J	`jnl`	Spectrum of Variable-Random Trees. J. Artif. Intell. Res. Fei Tony Liu, Kai Ming Ting, Yang Yu, Zhi-Hua Zhou
2007	J	`jnl`	Classifying under computational resource constraints: anytime classification using probabilistic estimators. Mach. Learn. Ying Yang, Geoffrey I. Webb, Kevin B. Korb, Kai Ming Ting
2007	A*	`conf`	Cocktail Ensemble for Regression. ICDM Yang Yu, Zhi-Hua Zhou, Kai Ming Ting
2007	—	`conf`	Examining Dissimilarity Scaling in Ant Colony Approaches to Data Clustering. ACAL Swee Chuan Tan, Kai Ming Ting, Shyh Wei Teng
2007	J	`jnl`	To Select or To Weigh: A Comparative Study of Linear Combination Schemes for SuperParent-One-Dependence Estimators. IEEE Trans. Knowl. Data Eng. Ying Yang, Geoffrey I. Webb, Jesús Cerquides, Kevin B. Korb, Janice R. Boughton, Kai Ming Ting
2006	—	`conf`	Ehipasiko: A Content-based Image Indexing and Retrieval System. AMT Shyh Wei Teng, Kai Ming Ting
2006	B	`conf`	Reproducing the Results of Ant-based Clustering Without Using Ants. IEEE Congress on Evolutionary Computation Swee Chuan Tan, Kai Ming Ting, Shyh Wei Teng
2006	—	`conf`	To Select or To Weigh: A Comparative Study of Model Selection and Model Weighing for SPODE Ensembles. ECML Ying Yang, Geoffrey I. Webb, Jesús Cerquides, Kevin B. Korb, Janice R. Boughton, Kai Ming Ting
2006	B	`conf`	Variable Randomness in Decision Tree Ensembles. PAKDD Fei Tony Liu, Kai Ming Ting
2006	—	`conf`	z-SVM: An SVM for Improved Classification of Imbalanced Data. Australian Conference on Artificial Intelligence Tasadduq Imam, Kai Ming Ting, Joarder Kamruzzaman
2005	—	`conf`	Ensemble Selection for SuperParent-One-Dependence Estimators. Australian Conference on Artificial Intelligence Ying Yang, Kevin B. Korb, Kai Ming Ting, Geoffrey I. Webb
2005	B	`conf`	Maximizing Tree Diversity by Building Complete-Random Decision Trees. PAKDD Fei Tony Liu, Kai Ming Ting, Wei Fan
2005	J	`jnl`	On the Application of ROC Analysis to Predict Classification Performance Under Varying Class Distributions. Mach. Learn. Geoffrey I. Webb, Kai Ming Ting
2004	—	`conf`	Improving the Centered CUSUMS Statistic for Structural Break Detection in Time Series. Australian Conference on Artificial Intelligence Kwok Pan Pang, Kai Ming Ting
2004	—	`conf`	Matching Model Versus Single Model: A Study of the Requirement to Match Class Distribution Using Decision Trees. ECML Kai Ming Ting
2003	J	`jnl`	A Study of AdaBoost with Naive Bayesian Classifiers: Weakness and Improvement. Comput. Intell. Kai Ming Ting, Zijian Zheng
2003	A*	`conf`	Model Stability: A key factor in determining whether an algorithm produces an optimal model from a matching distribution. ICDM Kai Ming Ting, Regina Jing Ying Quek
2002	B	`conf`	A Study on the Effect of Class Distribution Using Cost-Sensitive Learning. Discovery Science Kai Ming Ting
2002	J	`jnl`	An Instance-Weighting Method to Induce Cost-Sensitive Trees. IEEE Trans. Knowl. Data Eng. Kai Ming Ting
2002	A*	`conf`	Issues in Classifier Evaluation using Optimal Cost Curves. ICML Kai Ming Ting
2000	A*	`conf`	A Comparative Study of Cost-Sensitive Boosting Algorithms. ICML Kai Ming Ting
2000	—	`conf`	An Empirical Study of MetaCost Using Boosting Algorithms. ECML Kai Ming Ting
1999	B	`conf`	A fuzzy neural network for data mining: dealing with the problem of small disjuncts. IJCNN Yakov Frayman, Kai Ming Ting, Lipo Wang
1999	B	`conf`	Improving the Performance of Boosting for Naive Bayesian Classification. PAKDD Kai Ming Ting, Zijian Zheng
1999	J	`jnl`	Issues in Stacked Generalization. J. Artif. Intell. Res. Kai Ming Ting, Ian H. Witten
1999	A*	`conf`	Lazy Bayesian Rules: A Lazy Semi-Naive Bayesian Learning Technique Competitive to Boosting Decision Trees. ICML Zijian Zheng, Geoffrey I. Webb, Kai Ming Ting
1999	J	`jnl`	Learning from Batched Data: Model Combination Versus Data Combination. Knowl. Inf. Syst. Kai Ming Ting, Boon Toh Low, Ian H. Witten
1998	B	`conf`	Boosting Cost-Sensitive Trees. Discovery Science Kai Ming Ting, Zijian Zheng
1998	—	`conf`	Boosting Trees for Cost-Sensitive Classifications. ECML Kai Ming Ting, Zijian Zheng
1998	—	`conf`	Inducing Cost-Sensitive Trees via Instance Weighting. PKDD Kai Ming Ting
1998	B	`conf`	Integrating boosting and stochastic attribute selection committees for further improving the performance of decision tree learning. ICTAI Zijian Zheng, Geoffrey I. Webb, Kai Ming Ting
1997	J	`jnl`	Decision Combination Based on the Characterisation of Predictive Accuracy. Intell. Data Anal. Kai Ming Ting
1997	J	`jnl`	Discretisation in Lazy Learning Algorithms. Artif. Intell. Rev. Kai Ming Ting
1997	—	`conf`	Model Combination in the Multiple-Data-Batches Scenario. ECML Kai Ming Ting, Boon Toh Low
1997	—	`conf`	Stacked Generalizations: When Does It Work? IJCAI (2) Kai Ming Ting, Ian H. Witten
1997	A*	`conf`	Stacking Bagged and Dagged Models. ICML Kai Ming Ting, Ian H. Witten
1996	A*	`conf`	The Characterisation of Predictive Accuracy and Decision Combination. ICML Kai Ming Ting
1995	C	`conf`	Towards using a Single Uniform Metric in Instance-Based Learning. ICCBR Kai Ming Ting
1994	—	`conf`	An M-of-N Rule Induction Algorithm and its Application to DNA Domain. HICSS (5) Kai Ming Ting