Detecting circumscribed lesions with the Hough transform. - Bennett R. Groshong; W. Philip Kegelmeyer (Image Processing, 1996)

The computation of cloud base height from paired whole-sky imaging cameras. - Mark C. Allmen; W. Philip Kegelmeyer (Mach. Vis. Appl., 1997)

Combination of Multiple Classifiers Using Local Accuracy Estimates. - Kevin S. Woods; W. Philip Kegelmeyer; Kevin W. Bowyer (IEEE Trans. Pattern Anal. Mach. Intell., 1997)

Learning Rules from Distributed Data. - Lawrence O. Hall; Nitesh V. Chawla; Kevin W. Bowyer; W. Philip Kegelmeyer (Large-Scale Parallel Data Mining, 1999)

A parallel decision tree builder for mining very large visualization datasets. - Kevin W. Bowyer; Lawrence O. Hall; Thomas Moore; Nitesh V. Chawla; W. Philip Kegelmeyer (SMC, 2000)

Bagging Is a Small-Data-Set Phenomenon. - Nitesh V. Chawla; Thomas E. Moore; Kevin W. Bowyer; Lawrence O. Hall; Clayton Springer; W. Philip Kegelmeyer (CVPR (2), 2001)

Feature Characterization in Scientific Datasets. - Elizabeth Bradley; Nancy Collins; W. Philip Kegelmeyer (IDA, 2001)

Investigation of bagging-like effects and decision trees versus neural nets in protein secondary structure prediction. - Nitesh V. Chawla; Thomas E. Moore; Kevin W. Bowyer; Lawrence O. Hall; Clayton Springer; W. Philip Kegelmeyer (BIOKDD, 2001)

Distributed Pasting of Small Votes. - Nitesh V. Chawla; Lawrence O. Hall; Kevin W. Bowyer; Thomas E. Moore; W. Philip Kegelmeyer (Multiple Classifier Systems, 2002)

SMOTE: Synthetic Minority Over-sampling Technique. - Nitesh V. Chawla; Kevin W. Bowyer; Lawrence O. Hall; W. Philip Kegelmeyer (J. Artif. Intell. Res., 2002)

Comparing Pure Parallel Ensemble Creation Techniques Against Bagging. - Lawrence O. Hall; Kevin W. Bowyer; Robert E. Banfield; Divya Bhadoria; W. Philip Kegelmeyer; Steven Eschrich (ICDM, 2003)

A New Ensemble Diversity Measure Applied to Thinning Ensembles. - Robert E. Banfield; Lawrence O. Hall; Kevin W. Bowyer; W. Philip Kegelmeyer (Multiple Classifier Systems, 2003)

Distributed learning with bagging-like performance. - Nitesh V. Chawla; Thomas E. Moore; Lawrence O. Hall; Kevin W. Bowyer; W. Philip Kegelmeyer; Clayton Springer (Pattern Recognit. Lett., 2003)

Creating and Managing "Lookmarks" in ParaView. - Eric T. Stanton; W. Philip Kegelmeyer (INFOVIS, 2004)

A Comparison of Ensemble Creation Techniques. - Robert E. Banfield; Lawrence O. Hall; Kevin W. Bowyer; Divya Bhadoria; W. Philip Kegelmeyer; Steven Eschrich (Multiple Classifier Systems, 2004)

Learning Ensembles from Bites: A Scalable and Accurate Approach. - Nitesh V. Chawla; Lawrence O. Hall; Kevin W. Bowyer; W. Philip Kegelmeyer (J. Mach. Learn. Res., 2004)

FCLib: A Library for Building Data Analysis and Data Discovery Tools. - Wendy S. Koegler; W. Philip Kegelmeyer (IDA, 2005)

Ensembles of Classifiers from Spatially Disjoint Data. - Robert E. Banfield; Lawrence O. Hall; Kevin W. Bowyer; W. Philip Kegelmeyer (Multiple Classifier Systems, 2005)

Ensemble diversity measures and their application to thinning. - Robert E. Banfield; Lawrence O. Hall; Kevin W. Bowyer; W. Philip Kegelmeyer (Inf. Fusion, 2005)

Learning to Predict Salient Regions from Disjoint and Skewed Training Sets. - Larry Shoemaker; Robert E. Banfield; Lawrence O. Hall; Kevin W. Bowyer; W. Philip Kegelmeyer (ICTAI, 2006)

Boosting Lite - Handling Larger Datasets and Slower Base Classifiers. - Lawrence O. Hall; Robert E. Banfield; Kevin W. Bowyer; W. Philip Kegelmeyer (MCS, 2007)

A Comparison of Decision Tree Ensemble Creation Techniques. - Robert E. Banfield; Lawrence O. Hall; Kevin W. Bowyer; W. Philip Kegelmeyer (IEEE Trans. Pattern Anal. Mach. Intell., 2007)

Semi-supervised learning on large complex simulations. - John Nicholas Korecki; Robert E. Banfield; Larry O. Hall; Kevin W. Bowyer; W. Philip Kegelmeyer (ICPR, 2008)

Detecting and ordering salient regions for efficient browsing. - Larry Shoemaker; Robert E. Banfield; Larry O. Hall; Kevin W. Bowyer; W. Philip Kegelmeyer (ICPR, 2008)

Feature selection via decision tree surrogate splits. - Clayton Springer; W. Philip Kegelmeyer (ICPR, 2008)

Using classifier ensembles to label spatially disjoint data. - Larry Shoemaker; Robert E. Banfield; Lawrence O. Hall; Kevin W. Bowyer; W. Philip Kegelmeyer (Inf. Fusion, 2008)

Terrain Segmentation with On-Line Mixtures of Experts for Autonomous Robot Navigation. - Michael J. Procopio; W. Philip Kegelmeyer; Gregory Z. Grudic; Jane Mulligan (MCS, 2009)

Multilinear Algebra for Analyzing Data with Multiple Linkages. - Daniel M. Dunlavy; Tamara G. Kolda; W. Philip Kegelmeyer (Graph Algorithms in the Language of Linear Algebra, 2011)

Multilingual Sentiment Analysis Using Latent Semantic Indexing and Machine Learning. - Brett W. Bader; W. Philip Kegelmeyer; Peter A. Chew (ICDM Workshops, 2011)

COMET: A Recipe for Learning and Using Large Ensembles on Massive Data. - Justin D. Basilico; M. Arthur Munson; Tamara G. Kolda; Kevin R. Dixon; W. Philip Kegelmeyer (ICDM, 2011)

Detecting and ordering salient regions. - Larry Shoemaker; Robert E. Banfield; Lawrence O. Hall; Kevin W. Bowyer; W. Philip Kegelmeyer (Data Min. Knowl. Discov., 2011)

Exploring Topic Coherence over Many Models and Many Topics. - Keith Stevens; W. Philip Kegelmeyer; David Andrzejewski; David Buttler (EMNLP-CoNLL, 2012)

Hellinger distance decision trees are robust and skew-insensitive. - David A. Cieslak; T. Ryan Hoens; Nitesh V. Chawla; W. Philip Kegelmeyer (Data Min. Knowl. Discov., 2012)

Streaming Malware Classification in the Presence of Concept Drift and Class Imbalance. - W. Philip Kegelmeyer; Ken Chiang; Joey Burton Ingram (ICMLA (2), 2013)

Attacking DBSCAN for Fun and Profit. - Jonathan Crussell; W. Philip Kegelmeyer (SDM, 2015)

Using feature importance metrics to detect events of interest in scientific computing applications. - Julia Ling; W. Philip Kegelmeyer; Aditya Konduri; Hemanth Kolla; Kevin A. Reed; Timothy M. Shead; Warren L. Davis IV (LDAV, 2017)

A Review of Machine Learning Applications in Fuzzing. - Gary J. Saavedra; Kathryn N. Rodhouse; Daniel M. Dunlavy; W. Philip Kegelmeyer (CoRR, 2019)

Anomaly detection in scientific data using joint statistical moments. - Aditya Konduri; Hemanth Kolla; W. Philip Kegelmeyer; Timothy M. Shead; Julia Ling; Warren L. Davis IV (J. Comput. Phys., 2019)

A hybrid deep learning architecture for classification of microscopic damage on National Ignition Facility laser optics. - Connor Amorin; Laura M. Kegelmeyer; W. Philip Kegelmeyer (Stat. Anal. Data Min., 2019)

Mind the Gap: On Bridging the Semantic Gap between Machine Learning and Malware Analysis. - Michael R. Smith 002; Nicholas T. Johnson; Joe B. Ingram; Armida J. Carbajal; Bridget I. Haus; Eva Domschot; Ramyaa; Christopher C. Lamb; Stephen J. Verzi; W. Philip Kegelmeyer (AISec@CCS, 2020)

Mind the Gap: On Bridging the Semantic Gap between Machine Learning and Information Security. - Michael R. Smith 002; Nicholas T. Johnson; Joe B. Ingram; Armida J. Carbajal; Ramyaa Ramyaa; Evelyn Domschot; Christopher C. Lamb; Stephen J. Verzi; W. Philip Kegelmeyer (CoRR, 2020)