| SMOTE: synthetic minority over-sampling technique NV Chawla, KW Bowyer, LO Hall, WP Kegelmeyer Journal of artificial intelligence research 16, 321-357, 2002 | 13425 | 2002 |
| Current status of the digital database for screening mammography M Heath, K Bowyer, D Kopans, P Kegelmeyer, R Moore, K Chang, ... Digital mammography, 457-460, 1998 | 1644 | 1998 |
| Combination of multiple classifiers using local accuracy estimates K Woods, WP Kegelmeyer, K Bowyer IEEE transactions on pattern analysis and machine intelligence 19 (4), 405-410, 1997 | 1268 | 1997 |
| Data mining: Practical machine learning tools and techniques HW Ian, F Eibe Morgan Kaufmann Publishers, 2005 | 1159* | 2005 |
| A comparison of decision tree ensemble creation techniques RE Banfield, LO Hall, KW Bowyer, WP Kegelmeyer IEEE transactions on pattern analysis and machine intelligence 29 (1), 173-180, 2006 | 476 | 2006 |
| Computer-aided mammographic screening for spiculated lesions. WP Kegelmeyer Jr, JM Pruneda, PD Bourland, A Hillis, MW Riggs, ... Radiology 191 (2), 331-337, 1994 | 443 | 1994 |
| Multi-instance multi-label learning ZH Zhou, ML Zhang, SJ Huang, YF Li Artificial Intelligence 176 (1), 2291-2320, 2012 | 403 | 2012 |
| Exploring topic coherence over many models and many topics K Stevens, P Kegelmeyer, D Andrzejewski, D Buttler Proceedings of the 2012 joint conference on empirical methods in natural …, 2012 | 351 | 2012 |
| Ensemble diversity measures and their application to thinning RE Banfield, LO Hall, KW Bowyer, WP Kegelmeyer Information Fusion 6 (1), 49-62, 2005 | 259 | 2005 |
| Comparative evaluation of pattern recognition techniques for detection of microcalcifications in mammography KS Woods, CC Doss, KW Bowyer, JL Solka, CE Priebe, ... International Journal of Pattern Recognition and Artificial Intelligence 7 …, 1993 | 233 | 1993 |
| Data mining for scientific and engineering applications RL Grossman, C Kamath, P Kegelmeyer, V Kumar, R Namburu Springer Science & Business Media, 2013 | 229 | 2013 |
| Hellinger distance decision trees are robust and skew-insensitive DA Cieslak, TR Hoens, NV Chawla, WP Kegelmeyer Data Mining and Knowledge Discovery 24 (1), 136-158, 2012 | 193 | 2012 |
| Learning ensembles from bites: A scalable and accurate approach NV Chawla, LO Hall, KW Bowyer, WP Kegelmeyer The Journal of Machine Learning Research 5, 421-451, 2004 | 167 | 2004 |
| A new ensemble diversity measure applied to thinning ensembles RE Banfield, LO Hall, KW Bowyer, WP Kegelmeyer International Workshop on Multiple Classifier Systems, 306-316, 2003 | 98 | 2003 |
| Method and apparatus for detecting a desired behavior in digital image data WP Kegelmeyer Jr US Patent 5,633,948, 1997 | 91 | 1997 |
| Multilinear algebra for analyzing data with multiple linkages DM Dunlavy, TG Kolda, WP Kegelmeyer Graph algorithms in the language of linear algebra, 85-114, 2011 | 82 | 2011 |
| Computer detection of stellate lesions in mammograms WP Kegelmeyer Jr Biomedical Image Processing and Three-Dimensional Microscopy 1660, 446-454, 1992 | 81 | 1992 |
| Distributed learning with bagging-like performance NV Chawla, TE Moore, LO Hall, KW Bowyer, WP Kegelmeyer, C Springer Pattern recognition letters 24 (1-3), 455-471, 2003 | 78 | 2003 |
| Evaluation of stellate lesion detection in a standard mammogram data set WP Kegelmeyer Jr International Journal of Pattern Recognition and Artificial Intelligence 7 …, 1993 | 74 | 1993 |
| Evaluation of stellate lesion detection in a standard mammogram data set WP Kegelmeyer Jr International Journal of Pattern Recognition and Artificial Intelligence 7 …, 1993 | 74 | 1993 |
