W. Philip Kegelmeyer

According to our database, W. Philip Kegelmeyer authored at least 41 papers between 1996 and 2020.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2020

Mind the Gap: On Bridging the Semantic Gap between Machine Learning and Information Security.

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

CoRR, 2020

Mind the Gap: On Bridging the Semantic Gap between Machine Learning and Malware Analysis.

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

Proceedings of the AISec@CCS 2020: Proceedings of the 13th ACM Workshop on Artificial Intelligence and Security, 2020

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

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 Review of Machine Learning Applications in Fuzzing.

Gary J. Saavedra, Kathryn N. Rodhouse, Daniel M. Dunlavy, W. Philip Kegelmeyer

CoRR, 2019

2017

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

Proceedings of the 7th IEEE Symposium on Large Data Analysis and Visualization, 2017

2015

Attacking DBSCAN for Fun and Profit.

Jonathan Crussell, W. Philip Kegelmeyer

Proceedings of the 2015 SIAM International Conference on Data Mining, Vancouver, BC, Canada, April 30, 2015

2013

Streaming Malware Classification in the Presence of Concept Drift and Class Imbalance.

W. Philip Kegelmeyer, Ken Chiang, Joey Burton Ingram

Proceedings of the 12th International Conference on Machine Learning and Applications, 2013

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

Exploring Topic Coherence over Many Models and Many Topics.

Keith Stevens, W. Philip Kegelmeyer, David Andrzejewski, David Buttler

Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, 2012

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

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

Proceedings of the 11th IEEE International Conference on Data Mining, 2011

Multilingual Sentiment Analysis Using Latent Semantic Indexing and Machine Learning.

Brett W. Bader, W. Philip Kegelmeyer, Peter A. Chew

Proceedings of the Data Mining Workshops (ICDMW), 2011

Multilinear Algebra for Analyzing Data with Multiple Linkages.

Daniel M. Dunlavy, Tamara G. Kolda, W. Philip Kegelmeyer

Proceedings of the Graph Algorithms in the Language of Linear Algebra, 2011

2009

Terrain Segmentation with On-Line Mixtures of Experts for Autonomous Robot Navigation.

Michael J. Procopio, W. Philip Kegelmeyer, Gregory Z. Grudic, Jane Mulligan

Proceedings of the Multiple Classifier Systems, 8th International Workshop, 2009

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

Feature selection via decision tree surrogate splits.

Clayton Springer, W. Philip Kegelmeyer

Proceedings of the 19th International Conference on Pattern Recognition (ICPR 2008), 2008

Detecting and ordering salient regions for efficient browsing.

Larry Shoemaker, Robert E. Banfield, Larry O. Hall, Kevin W. Bowyer, W. Philip Kegelmeyer

Proceedings of the 19th International Conference on Pattern Recognition (ICPR 2008), 2008

Semi-supervised learning on large complex simulations.

John Nicholas Korecki, Robert E. Banfield, Larry O. Hall, Kevin W. Bowyer, W. Philip Kegelmeyer

Proceedings of the 19th International Conference on Pattern Recognition (ICPR 2008), 2008

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

Boosting Lite - Handling Larger Datasets and Slower Base Classifiers.

Lawrence O. Hall, Robert E. Banfield, Kevin W. Bowyer, W. Philip Kegelmeyer

Proceedings of the Multiple Classifier Systems, 7th International Workshop, 2007

2006

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

Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2006), 2006

2005

Ensemble diversity measures and their application to thinning.

Robert E. Banfield, Lawrence O. Hall, Kevin W. Bowyer, W. Philip Kegelmeyer

Inf. Fusion, 2005

Ensembles of Classifiers from Spatially Disjoint Data.

Robert E. Banfield, Lawrence O. Hall, Kevin W. Bowyer, W. Philip Kegelmeyer

Proceedings of the Multiple Classifier Systems, 6th International Workshop, 2005

FCLib: A Library for Building Data Analysis and Data Discovery Tools.

Wendy S. Koegler, W. Philip Kegelmeyer

Proceedings of the Advances in Intelligent Data Analysis VI, 2005

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

A Comparison of Ensemble Creation Techniques.

Robert E. Banfield, Lawrence O. Hall, Kevin W. Bowyer, Divya Bhadoria, W. Philip Kegelmeyer, Steven Eschrich

Proceedings of the Multiple Classifier Systems, 5th International Workshop, 2004

Creating and Managing "Lookmarks" in ParaView.

Eric T. Stanton, W. Philip Kegelmeyer

Proceedings of the 10th IEEE Symposium on Information Visualization (InfoVis 2004), 2004

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

A New Ensemble Diversity Measure Applied to Thinning Ensembles.

Robert E. Banfield, Lawrence O. Hall, Kevin W. Bowyer, W. Philip Kegelmeyer

Proceedings of the Multiple Classifier Systems, 4th International Workshop, 2003

Comparing Pure Parallel Ensemble Creation Techniques Against Bagging.

Lawrence O. Hall, Kevin W. Bowyer, Robert E. Banfield, Divya Bhadoria, W. Philip Kegelmeyer, Steven Eschrich

Proceedings of the 3rd IEEE International Conference on Data Mining (ICDM 2003), 2003

2002

SMOTE: Synthetic Minority Over-sampling Technique.

Nitesh V. Chawla, Kevin W. Bowyer, Lawrence O. Hall, W. Philip Kegelmeyer

J. Artif. Intell. Res., 2002

Distributed Pasting of Small Votes.

Nitesh V. Chawla, Lawrence O. Hall, Kevin W. Bowyer, Thomas E. Moore, W. Philip Kegelmeyer

Proceedings of the Multiple Classifier Systems, Third International Workshop, 2002

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

Proceedings of the ACM SIGKDD Workshop on Data Mining in Bioinformatics (BIOKDD 2001), 2001

Feature Characterization in Scientific Datasets.

Elizabeth Bradley, Nancy Collins, W. Philip Kegelmeyer

Proceedings of the Advances in Intelligent Data Analysis, 4th International Conference, 2001

Bagging Is a Small-Data-Set Phenomenon.

Nitesh V. Chawla, Thomas E. Moore, Kevin W. Bowyer, Lawrence O. Hall, Clayton Springer, W. Philip Kegelmeyer

Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2001), 2001

2000

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

Proceedings of the IEEE International Conference on Systems, 2000

1999

Learning Rules from Distributed Data.

Lawrence O. Hall, Nitesh V. Chawla, Kevin W. Bowyer, W. Philip Kegelmeyer

Proceedings of the Large-Scale Parallel Data Mining, 1999

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

The computation of cloud base height from paired whole-sky imaging cameras.

Mark C. Allmen, W. Philip Kegelmeyer

Mach. Vis. Appl., 1997

1996

Detecting circumscribed lesions with the Hough transform.

Bennett R. Groshong, W. Philip Kegelmeyer

Proceedings of the Medical Imaging 1996: Image Processing, 1996