"Learning ensembles from bites: A scalable and accurate approach", Nitesh V. Chawla, Lawrence O. Hall, KevinW. Bowyer, W. Philip Kegelmeyer, Journal of Machine Learning Research, 2003

"SMOTEBoost: Improving Prediction of the Minority Class in Boosting" N.V. Chawla, A. Lazarevic, L.O. Hall, and K.W. Bowyer, 7th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD), pp. 107 to 119, Dubrovnik, Croatia,2003.

"Comparing Pure Parallel Ensemble Creation Techniques Against Bagging" Lawrence O. Hall, KevinW. Bowyer, Robert E. Banfield, Divya Bhadoria, W. Philip Kegelmeyer and Steven Eschrich, The Third IEEE International Conference on Data Mining, Melbourne, Florida, pp. 533-536, November, 2003.

"Why are Neural Networks Sometimes Much More Accurate than Decision Trees: An Analysis on a Bio-Informatics Problem" Lawrence O. Hall, Xiaomei Liu, Kevin W. Bowyer, Robert Banfield,  IEEE International Conference on Systems, Man & Cybernetics, Washington, D.C., pp. 2851-2856, October 5–8, 2003

"A New Ensemble Diversity Measure Applied to Thinning Ensembles" Robert E. Banfield, Lawrence O. Hall, Kevin W. Bowyer, W. Philip Kegelmeyer, International Workshop on Multiple Classifier Systems, pp. 306 - 316, Surrey, UK, June, 2003.

"Is Error-Based Pruning Redeemable?" Lawrence O. Hall, Kevin W. Bowyer, Robert E. Banfield, Steven Eschrich, Richard Collins, International Journal on Artificial Intelligence Tools: Architectures, Languages, Algorithms, V. 12, No. 3, pp. 249-264, 2003.

"An Analysis of Neural Network Versus Decision Tree Performance on a Bio-Informatics Problem" Lawrence O. Hall, Xiaomei Liu, Kevin W. Bowyer, Robert Banfield, Workshop on Information Technology, Rabat, Morocco - March 17 - 19, 2003

"Error Based Pruning of Decision Trees grown on very datasets can work!" Lawrence O. Hall, Kevin W. Bowyer, Richard Collins, Robert Banfield, International Conference on Tools for Artificial Intelligence, pp. 233-238, November 2002.

"Learning on extremes -- size and imbalance -- of data." Nitesh V. Chawla, Dissertation Defense Slides, August 2, 2002, Department of Computer Science and Engineering, University of South Florida.

"Generalization Methods in Bioinformatics", Steven Eschrich, Nitesh V. Chawla, Lawrence O. Hall, BIOKDD 2002 (Workshop on Bioinformatics, 8th ACM SIGKDD Conference).

"SMOTE: Synthetic Minority Over-sampling technique", Nitesh Chawla, Kevin Bowyer, Lawrence Hall, Philip Kegelmeyer, Journal of Artificial Intelligence Research , Volume 16, 321 -- 357, 2002. (This paper is available online from the JAIR site.)

"Distributed Pasting of Small Votes" Nitesh V. Chawla, Lawrence O. Hall, Kevin W. Bowyer, Thomas E. Moore, W. Philip Kegelmeyer. International Workshop on Multiple Classifier Systems, 2002 .

"Distributed learning with bagging-like performance", Nitesh V. Chawla, Thomas E. Moore, Lawrence O. Hall, Kevin W. Bowyer, W. Philip Kegelmeyer, Clayton Springer. Published in Pattern Recognition Letters, Vol. 24 (1-3) (2003) pp. 455-471

"Extreme Data Mining: What to do with a flood of training data? " Nitesh  Chawla, Lawrence Hall, Kevin Bowyer, Thomas Moore, Clayton Springer, Philip Kegelmeyer, Invited Talk, Conference of Institute of Operations Research and Management Sciences, (INFORMS) , 2001 .

"Creating Ensembles of Classifiers" Nitesh Chawla, Steven Eschrich, Lawrence Hall, IEEE International Conference on Data Mining (ICDM), November 2001.

For the extended version of the ICDM paper "Creating Ensembles of Classifiers", Go to Technical Report: Creating Ensembles of Classifiers.

"Bagging is a Small-Data-Set phenomenon" Nitesh Chawla, Thomas  Moore, Kevin Bowyer, Lawrence Hall, Clayton Springer, Philip Kegelmeyer. International Conference on Computer Vision and Pattern Recognition (CVPR), 2001

"Investigation of bagging-like effects and decision trees versus neural nets in protein secondary structure prediction", Nitesh  Chawla, Thomas Moore, Kevin Bowyer, Lawrence Hall, Clayton Springer, Philip Kegelmeyer. Workshop on Data Mining in Bioinformatics, Knowledge Discovery and Data Mining (KDD), 2001

"SMOTE: Synthetic Minority Over-sampling technique", Nitesh Chawla, Kevin Bowyer, Lawrence Hall, Philip Kegelmeyer, International Conference on Knowledge Based Computer Systems , 2000. 

Distributed Learning on Very Large Data Sets, L.O. Hall, K.W. Bowyer, W.P. Kegelmeyer, T.E. Moore and C. Chao, Workshop on Distributed and Parallel Knowledge Discovery, International Conference Knowledge Discovery and Data Mining (KDD), 2000.

"A Parallel Decision Tree Builder for Mining Very Large Visualization DataSets", Bowyer, Hall, Chawla, Moore, Kegelmeyer. IEEE International Conference on Systems, Man, and Cybernetics, 2000.

"AVATAR -- Adaptive Visualization Aid for Touring and Recovery," Lawrence O. Hall, Kevein W. Bowyer, Nitesh V. Chawla, Thomas E. Moore, W. Philip Kegelmeyer, Sandia - Report, SAND2000-8203, Sandia National Labs , 2000.

"RiDE : Rule Learning in Distributed Environment ", Nitesh Chawla, Master's Thesis, 1999.

"Learning rules from distributed data," Lawrence O. Hall, Nitesh Chawla, Kevin W. Bowyer, W. Philip Kegelmeyer, Large-Scale Parallel KDD Systems, International Conference of Knowledge Discovery and Data Mining . Lecture Notes in Computer Science,Springer Verlag, 1999.
 

 "Modifying MUSTAFA to capture salient data"   Nitesh Chawla, Lawrence O, Hall. Internal Tech Report, ISL-99-01, Dept. Of Computer Science and Engineerin g, University of South Florida, 1999.
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"Combining Decision Trees Learned in Parallel ", Lawrence O. Hall, Nitesh Chawla and Kevin W. Bowyer. Distributed Data Mining Workshop at International Conference of Knowledge Discovery and Data Mining , 1998.

"Decision Tree Learning on Very Large Data Sets," Lawrence O. Hall, Nitesh Chawla and Kevin W. Bowyer. IEEE International Conference on Systems, Man and Cybernetics , 1998.
 

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