W. Philip Kegelmeyer

44 papers A* 3A 1B 8Journal 16Unranked 15
YearRankTypeTitle / Venue / Authors
2020 J jnl
Mind the Gap: On Bridging the Semantic Gap between Machine Learning and Information Security.
CoRR
Michael R. Smith, Nicholas T. Johnson, Joe B. Ingram, Armida J. Carbajal, Ramyaa Ramyaa, Evelyn Domschot, Christopher C. Lamb, Stephen J. Verzi, W. Philip Kegelmeyer
2020 — conf
Mind the Gap: On Bridging the Semantic Gap between Machine Learning and Malware Analysis.
AISec@CCS
Michael R. Smith, Nicholas T. Johnson, Joe B. Ingram, Armida J. Carbajal, Bridget I. Haus, Eva Domschot, Ramyaa, Christopher C. Lamb, Stephen J. Verzi, W. Philip Kegelmeyer
2019 J jnl
A Review of Machine Learning Applications in Fuzzing.
CoRR
Gary J. Saavedra, Kathryn N. Rodhouse, Daniel M. Dunlavy, W. Philip Kegelmeyer
2019 J jnl
A hybrid deep learning architecture for classification of microscopic damage on National Ignition Facility laser optics.
Stat. Anal. Data Min.
Connor Amorin, Laura M. Kegelmeyer, W. Philip Kegelmeyer
2019 J jnl
Anomaly detection in scientific data using joint statistical moments.
J. Comput. Phys.
Aditya Konduri, Hemanth Kolla, W. Philip Kegelmeyer, Timothy M. Shead, Julia Ling, Warren L. Davis IV
2017 — conf
Using feature importance metrics to detect events of interest in scientific computing applications.
LDAV
Julia Ling, W. Philip Kegelmeyer, Aditya Konduri, Hemanth Kolla, Kevin A. Reed, Timothy M. Shead, Warren L. Davis IV
2015 A conf
Attacking DBSCAN for Fun and Profit.
SDM
Jonathan Crussell, W. Philip Kegelmeyer
2013 — conf
Streaming Malware Classification in the Presence of Concept Drift and Class Imbalance.
ICMLA (2)
W. Philip Kegelmeyer, Ken Chiang, Joey Burton Ingram
2012 — conf
Exploring Topic Coherence over Many Models and Many Topics.
EMNLP-CoNLL
Keith Stevens, W. Philip Kegelmeyer, David Andrzejewski, David Buttler
2012 J jnl
Hellinger distance decision trees are robust and skew-insensitive.
Data Min. Knowl. Discov.
David A. Cieslak, T. Ryan Hoens, Nitesh V. Chawla, W. Philip Kegelmeyer
2011 J jnl
COMET: A Recipe for Learning and Using Large Ensembles on Massive Data
CoRR
Justin D. Basilico, M. Arthur Munson, Tamara G. Kolda, Kevin R. Dixon, W. Philip Kegelmeyer
2011 A* conf
COMET: A Recipe for Learning and Using Large Ensembles on Massive Data.
ICDM
Justin D. Basilico, M. Arthur Munson, Tamara G. Kolda, Kevin R. Dixon, W. Philip Kegelmeyer
2011 J jnl
Detecting and ordering salient regions.
Data Min. Knowl. Discov.
Larry Shoemaker, Robert E. Banfield, Lawrence O. Hall, Kevin W. Bowyer, W. Philip Kegelmeyer
2011 — ch.
Multilinear Algebra for Analyzing Data with Multiple Linkages.
Graph Algorithms in the Language of Linear Algebra
Daniel M. Dunlavy, Tamara G. Kolda, W. Philip Kegelmeyer
2011 — conf
Multilingual Sentiment Analysis Using Latent Semantic Indexing and Machine Learning.
ICDM Workshops
Brett W. Bader, W. Philip Kegelmeyer, Peter A. Chew
2011 J jnl
SMOTE: Synthetic Minority Over-sampling Technique
CoRR
Kevin W. Bowyer, Nitesh V. Chawla, Lawrence O. Hall, W. Philip Kegelmeyer
2009 — conf
Terrain Segmentation with On-Line Mixtures of Experts for Autonomous Robot Navigation.
MCS
Michael J. Procopio, W. Philip Kegelmeyer, Gregory Z. Grudic, Jane Mulligan
2008 B conf
Detecting and ordering salient regions for efficient browsing.
ICPR
Larry Shoemaker, Robert E. Banfield, Larry O. Hall, Kevin W. Bowyer, W. Philip Kegelmeyer
2008 B conf
Feature selection via decision tree surrogate splits.
ICPR
Clayton Springer, W. Philip Kegelmeyer
2008 B conf
Semi-supervised learning on large complex simulations.
ICPR
John Nicholas Korecki, Robert E. Banfield, Larry O. Hall, Kevin W. Bowyer, W. Philip Kegelmeyer
2008 J jnl
Using classifier ensembles to label spatially disjoint data.
Inf. Fusion
Larry Shoemaker, Robert E. Banfield, Lawrence O. Hall, Kevin W. Bowyer, W. Philip Kegelmeyer
2007 J jnl
A Comparison of Decision Tree Ensemble Creation Techniques.
IEEE Trans. Pattern Anal. Mach. Intell.
Robert E. Banfield, Lawrence O. Hall, Kevin W. Bowyer, W. Philip Kegelmeyer
2007 — conf
Boosting Lite - Handling Larger Datasets and Slower Base Classifiers.
MCS
Lawrence O. Hall, Robert E. Banfield, Kevin W. Bowyer, W. Philip Kegelmeyer
2006 B conf
Learning to Predict Salient Regions from Disjoint and Skewed Training Sets.
ICTAI
Larry Shoemaker, Robert E. Banfield, Lawrence O. Hall, Kevin W. Bowyer, W. Philip Kegelmeyer
2005 J jnl
Ensemble diversity measures and their application to thinning.
Inf. Fusion
Robert E. Banfield, Lawrence O. Hall, Kevin W. Bowyer, W. Philip Kegelmeyer
2005 — conf
Ensembles of Classifiers from Spatially Disjoint Data.
Multiple Classifier Systems
Robert E. Banfield, Lawrence O. Hall, Kevin W. Bowyer, W. Philip Kegelmeyer
2005 B conf
FCLib: A Library for Building Data Analysis and Data Discovery Tools.
IDA
Wendy S. Koegler, W. Philip Kegelmeyer
2004 — conf
A Comparison of Ensemble Creation Techniques.
Multiple Classifier Systems
Robert E. Banfield, Lawrence O. Hall, Kevin W. Bowyer, Divya Bhadoria, W. Philip Kegelmeyer, Steven Eschrich
2004 — conf
Creating and Managing "Lookmarks" in ParaView.
INFOVIS
Eric T. Stanton, W. Philip Kegelmeyer
2004 J jnl
Learning Ensembles from Bites: A Scalable and Accurate Approach.
J. Mach. Learn. Res.
Nitesh V. Chawla, Lawrence O. Hall, Kevin W. Bowyer, W. Philip Kegelmeyer
2003 — conf
A New Ensemble Diversity Measure Applied to Thinning Ensembles.
Multiple Classifier Systems
Robert E. Banfield, Lawrence O. Hall, Kevin W. Bowyer, W. Philip Kegelmeyer
2003 A* conf
Comparing Pure Parallel Ensemble Creation Techniques Against Bagging.
ICDM
Lawrence O. Hall, Kevin W. Bowyer, Robert E. Banfield, Divya Bhadoria, W. Philip Kegelmeyer, Steven Eschrich
2003 J jnl
Distributed learning with bagging-like performance.
Pattern Recognit. Lett.
Nitesh V. Chawla, Thomas E. Moore, Lawrence O. Hall, Kevin W. Bowyer, W. Philip Kegelmeyer, Clayton Springer
2002 — conf
Distributed Pasting of Small Votes.
Multiple Classifier Systems
Nitesh V. Chawla, Lawrence O. Hall, Kevin W. Bowyer, Thomas E. Moore, W. Philip Kegelmeyer
2002 J jnl
SMOTE: Synthetic Minority Over-sampling Technique.
J. Artif. Intell. Res.
Nitesh V. Chawla, Kevin W. Bowyer, Lawrence O. Hall, W. Philip Kegelmeyer
2001 — conf
Bagging Is a Small-Data-Set Phenomenon.
CVPR (2)
Nitesh V. Chawla, Thomas E. Moore, Kevin W. Bowyer, Lawrence O. Hall, Clayton Springer, W. Philip Kegelmeyer
2001 B conf
Feature Characterization in Scientific Datasets.
IDA
Elizabeth Bradley, Nancy Collins, W. Philip Kegelmeyer
2001 — conf
Investigation of bagging-like effects and decision trees versus neural nets in protein secondary structure prediction.
BIOKDD
Nitesh V. Chawla, Thomas E. Moore, Kevin W. Bowyer, Lawrence O. Hall, Clayton Springer, W. Philip Kegelmeyer
2000 B conf
A parallel decision tree builder for mining very large visualization datasets.
SMC
Kevin W. Bowyer, Lawrence O. Hall, Thomas Moore, Nitesh V. Chawla, W. Philip Kegelmeyer
1999 — conf
Learning Rules from Distributed Data.
Large-Scale Parallel Data Mining
Lawrence O. Hall, Nitesh V. Chawla, Kevin W. Bowyer, W. Philip Kegelmeyer
1997 J jnl
Combination of Multiple Classifiers Using Local Accuracy Estimates.
IEEE Trans. Pattern Anal. Mach. Intell.
Kevin S. Woods, W. Philip Kegelmeyer, Kevin W. Bowyer
1997 J jnl
The computation of cloud base height from paired whole-sky imaging cameras.
Mach. Vis. Appl.
Mark C. Allmen, W. Philip Kegelmeyer
1996 A* conf
Combination of Multiple Classifiers Using Local Accuracy Estimates.
CVPR
Kevin S. Woods, Kevin W. Bowyer, W. Philip Kegelmeyer
1996 B conf
Detecting circumscribed lesions with the Hough transform.
Image Processing
Bennett R. Groshong, W. Philip Kegelmeyer