W. Philip Kegelmeyer

Publication Activity (10 Years)

Years Active: 1996-2020
Publications (10 Years): 6

Top Topics

Machine Learning

Class Imbalance

Scientific Computing

Top Venues

Stat. Anal. Data Min.

Publications

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
Mind the Gap: On Bridging the Semantic Gap between Machine Learning and Malware Analysis. AISec@CCS (2020)
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
Mind the Gap: On Bridging the Semantic Gap between Machine Learning and Information Security. CoRR (2020)
Aditya Konduri, Hemanth Kolla, W. Philip Kegelmeyer, Timothy M. Shead, Julia Ling, Warren L. Davis IV
Anomaly detection in scientific data using joint statistical moments. J. Comput. Phys. 387 (2019)
Connor Amorin, Laura M. Kegelmeyer, W. Philip Kegelmeyer
A hybrid deep learning architecture for classification of microscopic damage on National Ignition Facility laser optics. Stat. Anal. Data Min. 12 (6) (2019)
Gary J. Saavedra, Kathryn N. Rodhouse, Daniel M. Dunlavy, W. Philip Kegelmeyer
A Review of Machine Learning Applications in Fuzzing. CoRR (2019)
Julia Ling, W. Philip Kegelmeyer, Aditya Konduri, Hemanth Kolla, Kevin A. Reed, Timothy M. Shead, Warren L. Davis IV
Using feature importance metrics to detect events of interest in scientific computing applications. LDAV (2017)
Jonathan Crussell, W. Philip Kegelmeyer
Attacking DBSCAN for Fun and Profit. SDM (2015)
W. Philip Kegelmeyer, Ken Chiang, Joey Burton Ingram
Streaming Malware Classification in the Presence of Concept Drift and Class Imbalance. ICMLA (2) (2013)
David A. Cieslak, T. Ryan Hoens, Nitesh V. Chawla, W. Philip Kegelmeyer
Hellinger distance decision trees are robust and skew-insensitive. Data Min. Knowl. Discov. 24 (1) (2012)
Keith Stevens, W. Philip Kegelmeyer, David Andrzejewski, David Buttler
Exploring Topic Coherence over Many Models and Many Topics. EMNLP-CoNLL (2012)
Larry Shoemaker, Robert E. Banfield, Lawrence O. Hall, Kevin W. Bowyer, W. Philip Kegelmeyer
Detecting and ordering salient regions. Data Min. Knowl. Discov. 22 (1-2) (2011)
Brett W. Bader, W. Philip Kegelmeyer, Peter A. Chew
Multilingual Sentiment Analysis Using Latent Semantic Indexing and Machine Learning. ICDM Workshops (2011)
Kevin W. Bowyer, Nitesh V. Chawla, Lawrence O. Hall, W. Philip Kegelmeyer
SMOTE: Synthetic Minority Over-sampling Technique CoRR (2011)
Justin D. Basilico, M. Arthur Munson, Tamara G. Kolda, Kevin R. Dixon, W. Philip Kegelmeyer
COMET: A Recipe for Learning and Using Large Ensembles on Massive Data CoRR (2011)
Justin D. Basilico, M. Arthur Munson, Tamara G. Kolda, Kevin R. Dixon, W. Philip Kegelmeyer
COMET: A Recipe for Learning and Using Large Ensembles on Massive Data. ICDM (2011)
Michael J. Procopio, W. Philip Kegelmeyer, Gregory Z. Grudic, Jane Mulligan
Terrain Segmentation with On-Line Mixtures of Experts for Autonomous Robot Navigation. MCS (2009)
John Nicholas Korecki, Robert E. Banfield, Larry O. Hall, Kevin W. Bowyer, W. Philip Kegelmeyer
Semi-supervised learning on large complex simulations. ICPR (2008)
Larry Shoemaker, Robert E. Banfield, Lawrence O. Hall, Kevin W. Bowyer, W. Philip Kegelmeyer
Using classifier ensembles to label spatially disjoint data. Inf. Fusion 9 (1) (2008)
Clayton Springer, W. Philip Kegelmeyer
Feature selection via decision tree surrogate splits. ICPR (2008)
Larry Shoemaker, Robert E. Banfield, Larry O. Hall, Kevin W. Bowyer, W. Philip Kegelmeyer
Detecting and ordering salient regions for efficient browsing. ICPR (2008)
Robert E. Banfield, Lawrence O. Hall, Kevin W. Bowyer, W. Philip Kegelmeyer
A Comparison of Decision Tree Ensemble Creation Techniques. IEEE Trans. Pattern Anal. Mach. Intell. 29 (1) (2007)
Lawrence O. Hall, Robert E. Banfield, Kevin W. Bowyer, W. Philip Kegelmeyer
Boosting Lite - Handling Larger Datasets and Slower Base Classifiers. MCS (2007)
Larry Shoemaker, Robert E. Banfield, Lawrence O. Hall, Kevin W. Bowyer, W. Philip Kegelmeyer
Learning to Predict Salient Regions from Disjoint and Skewed Training Sets. ICTAI (2006)
Robert E. Banfield, Lawrence O. Hall, Kevin W. Bowyer, W. Philip Kegelmeyer
Ensembles of Classifiers from Spatially Disjoint Data. Multiple Classifier Systems (2005)
Wendy S. Koegler, W. Philip Kegelmeyer
FCLib: A Library for Building Data Analysis and Data Discovery Tools. IDA (2005)
Robert E. Banfield, Lawrence O. Hall, Kevin W. Bowyer, W. Philip Kegelmeyer
Ensemble diversity measures and their application to thinning. Inf. Fusion 6 (1) (2005)
Robert E. Banfield, Lawrence O. Hall, Kevin W. Bowyer, Divya Bhadoria, W. Philip Kegelmeyer, Steven Eschrich
A Comparison of Ensemble Creation Techniques. Multiple Classifier Systems (2004)
Eric T. Stanton, W. Philip Kegelmeyer
Creating and Managing "Lookmarks" in ParaView. INFOVIS (2004)
Nitesh V. Chawla, Lawrence O. Hall, Kevin W. Bowyer, W. Philip Kegelmeyer
Learning Ensembles from Bites: A Scalable and Accurate Approach. J. Mach. Learn. Res. 5 (2004)
Robert E. Banfield, Lawrence O. Hall, Kevin W. Bowyer, W. Philip Kegelmeyer
A New Ensemble Diversity Measure Applied to Thinning Ensembles. Multiple Classifier Systems (2003)
Lawrence O. Hall, Kevin W. Bowyer, Robert E. Banfield, Divya Bhadoria, W. Philip Kegelmeyer, Steven Eschrich
Comparing Pure Parallel Ensemble Creation Techniques Against Bagging. ICDM (2003)
Nitesh V. Chawla, Thomas E. Moore, Lawrence O. Hall, Kevin W. Bowyer, W. Philip Kegelmeyer, Clayton Springer
Distributed learning with bagging-like performance. Pattern Recognit. Lett. 24 (1-3) (2003)
Nitesh V. Chawla, Kevin W. Bowyer, Lawrence O. Hall, W. Philip Kegelmeyer
SMOTE: Synthetic Minority Over-sampling Technique. J. Artif. Intell. Res. 16 (2002)
Nitesh V. Chawla, Lawrence O. Hall, Kevin W. Bowyer, Thomas E. Moore, W. Philip Kegelmeyer
Distributed Pasting of Small Votes. Multiple Classifier Systems (2002)
Elizabeth Bradley, Nancy Collins, W. Philip Kegelmeyer
Feature Characterization in Scientific Datasets. IDA (2001)
Nitesh V. Chawla, Thomas E. Moore, Kevin W. Bowyer, Lawrence O. Hall, Clayton Springer, W. Philip Kegelmeyer
Bagging Is a Small-Data-Set Phenomenon. CVPR (2) (2001)
Nitesh V. Chawla, Thomas E. Moore, Kevin W. Bowyer, Lawrence O. Hall, Clayton Springer, W. Philip Kegelmeyer
Investigation of bagging-like effects and decision trees versus neural nets in protein secondary structure prediction. BIOKDD (2001)
Kevin W. Bowyer, Lawrence O. Hall, Thomas Moore, Nitesh V. Chawla, W. Philip Kegelmeyer
A parallel decision tree builder for mining very large visualization datasets. SMC (2000)
Lawrence O. Hall, Nitesh V. Chawla, Kevin W. Bowyer, W. Philip Kegelmeyer
Learning Rules from Distributed Data. Large-Scale Parallel Data Mining (1999)
Mark C. Allmen, W. Philip Kegelmeyer
The computation of cloud base height from paired whole-sky imaging cameras. Mach. Vis. Appl. 9 (4) (1997)
Kevin S. Woods, W. Philip Kegelmeyer, Kevin W. Bowyer
Combination of Multiple Classifiers Using Local Accuracy Estimates. IEEE Trans. Pattern Anal. Mach. Intell. 19 (4) (1997)
Bennett R. Groshong, W. Philip Kegelmeyer
Detecting circumscribed lesions with the Hough transform. Medical Imaging: Image Processing (1996)
Kevin S. Woods, Kevin W. Bowyer, W. Philip Kegelmeyer
Combination of Multiple Classifiers Using Local Accuracy Estimates. CVPR (1996)