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DIAL Publications

2005

  1. Alec Pawling, Nitesh V. Chawla, and Amitabh Chaudhary. “Computing Information Gain in Data Streams.” Proceedings of the IEEE ICDM Workshop on Temporal Data Mining: Algorithms, Theory and Applications, pp. 72–81, 2005. PDF
  2. Nitesh V. Chawla. “Teaching Data Mining by Coalescing Theory and Applications.” Proceedings of the 35th Annual ASEE/IEEE Conference on Frontiers in Education (FIE), pp. S1J 17–23, 2005. PDF
  3. Nitesh V. Chawla and Kevin W. Bowyer. “Ensembles in Face Recognition: Tackling the Extremes of High Dimensionality, Temporality, and Variance in Data.” Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (SMC), vol. 3, pp. 2246–2351. 2005. PDF
  4. Nitesh V. Chawla. “Data Mining for Imbalanced Datasets: An Overview.” Data Mining and Knowledge Discovery Handbook: A Complete Guide for Practitioners and Researchers, pp. 853–867, 2005. PDF
  5. Nitesh V. Chawla, Lawrence O. Hall, and Ajay Joshi. “Wrapper-Based Computation and Evaluation of Sampling Methods for Imbalanced Datasets.” Proceedings of the ACM SIGKDD International Workshop on Utility-Based Data Mining (UBDM), pp. 24–33, 2005. PDF
  6. Jared Sylvester and Nitesh V. Chawla. “Evolutionary Ensembles: Combining Learning Agents Using Genetic Algorithms.” AAAI Workshop on Multi-Agent Systems, pp. 46–51, 2005. PDF
  7. Daniel Mack, Nitesh V. Chawla, and Gregory Madey. “Activity Mining in Open Source Software.” Proceedings of the Annual Conference of the North American Association for Computational Social and Organizational Science (NAACSOS), 2005. PDF
  8. Nitesh V. Chawla and Kevin W. Bowyer. “Random Subspaces and Subsampling for 2-D Face Recognition.” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), vol. 2, pp. 582–589, 2005. PDF
  9. Nitesh V. Chawla and Kevin W. Bowyer. “Designing Multiple Classifier Systems for Face Recognition.” Proceedings of the 6th International Workshop on Multiple Classifier Systems (MCS), pp. 407–416, 2005. PDF
  10. Nitesh V. Chawla and Grigoris J. Karakoulas. “Learning from Labeled and Unlabeled Data: An Empirical Study Across Techniques and Domains.” Journal of Artificial Intelligence Research (JAIR), vol. 23, 331–366, 2005. arXiv PDF

2004 – 1998

  1. Nitesh V. Chawla, Lawrence O. Hall, Kevin W. Bowyer, and W. Philip Kegelmeyer. “Learning Ensembles from Bites: A Scalable and Accurate Approach.” Journal of Machine Learning Research (JMLR), vol. 5, pp. 521–451, 2004. PDF
  2. Steven Eschrich, Nitesh V. Chawla, and Lawrence O. Hall. “Learning to Predict in Complex Biological Domains.” Journal of System Simulation, 14(11):1464–1471, 2004. PDF
  3. Predrag Radivojac, Nitesh V. Chawla, A. Keith Dunker, and Zoran Obradovic. “Classification and Knowledge Discovery in Protein Databases.” Journal of Biomedical Informatics (JBI), 37(4):224–239, 2004. PDF
  4. Nitesh V. Chawla, Nathalie Japkowicz, and Aleksander Kołcz. “Editorial: Special Issue on Learning From Imbalanced Datasets.” ACM SIGKDD Explorations, 6(1):1–6, 2004. PDF
  5. Nitesh V. Chawla, Grigoris Karakoulas, and Danny Roobaert. “Lessons Learned from Feature Selection Competition.” Proceedings of the NIPS Workshop on Feature Selection, 2003. PDF
  6. Nitesh V. Chawla, Aleksandar Lazarevic, Lawrence O. Hall, and Kevin W. Bowyer. “SMOTEBoost: Improving the Prediction of the Minority Class in Boosting.” Proceedings of the 14th European Conference on Machine Learning and the 7th European Conference on Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD), pp. 107–119, 2003. PDF
  7. Nitesh V. Chawla. “C4.5 and Imbalanced Data Sets: Investigating the Effect of Sampling Method, Probabilistic Estimate, and Decision Tree Structure.” Proceedings of the ICML Workshop on Learning from Imbalanced Data Sets II, vol. 3, 2003. PDF
  8. Nitesh V. Chawla, Thomas E. Moore Jr., Lawrence O. Hall, Kevin W. Bowyer, W. Philip Kegelmeyer, and Clayton Springer. “Distributed Learning with Bagging-Like Performance.” Pattern Recognition Letters, 24(1):455–471, 2003. PDF
  9. Steven Eschrich, Nitesh V. Chawla, and Lawrence O. Hall. “Generalization Methods in Bioinformatics.” Proceedings of the ACM SIGKDD Workshop on Data Mining in Bioinformatics (BIOKDD), vol. 2, pp. 25–32, 2002. PDF
  10. Nitesh V. Chawla, Lawrence O. Hall, Kevin W. Bowyer, Thomas E. Moore Jr., and W. Philip Kegelmeyer. “Distributed Pasting of Small Votes.” Proceedings of the 3rd International Workshop on Multiple Classifier Systems (MCS), pp. 52–61, 2002. PDF
  11. Nitesh V. Chawla, Kevin W. Bowyer, Lawrence O. Hall, and Philip Kegelmeyer. “SMOTE: Synthetic Minority Over-Sampling Technique.” Journal of Artificial Intelligence Research (JAIR), 16(1):321–357, 2002. PDF
  12. Nitesh V. Chawla, Thomas E. Moore Jr., Kevin W. Bowyer, Lawrence O. Hall, Clayton Springer, and W. Philip Kegelmeyer. “Bagging is a Small-Data-Set Phenomenon.” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), vol. 2, pp. 685–689, 2001. PDF
  13. Nitesh V. Chawla, Steven Eschrich, and Lawrence O. Hall. “Creating Ensembles of Classifiers.” Proceedings of the IEEE International Conference on Data Mining (ICDM), pp. 580–581, 2001. PDF
  14. Nitesh V. Chawla, Thomas E. Moore Jr., Kevin W. Bowyer, Lawrence O. Hall, Clayton Springer, and W. Philip Kegelmeyer. “Bagging-Like Effects and Decision Trees and Neural Nets in Protein Secondary Structure Prediction.” Proceedings of the ACM SIGKDD Workshop on Data Mining in Bioinformatics (BIOKDD), pp. 50–59, 2001. PDF
  15. Kevin W. Bowyer, Lawrence O. Hall, Thomas E. Moore Jr., Nitesh V. Chawla, and W. Phillip Kegelmeyer. “A Parallel Decision Tree Builder for Mining Very Large Visualization Datasets.” Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (SMC), vol. 3, pp. 1888–1893, 2000. PDF
  16. Lawrence O. Hall, Nitesh V. Chawla, Kevin W. Bowyer, and W. Philip Kegelmeyer. “Learning Rules from Distributed Data.” Large-Scale Parallel Data Mining, pp. 211–220, 2000. PDF
  17. Lawrence O. Hall, Nitesh V. Chawla, and Kevin W. Bowyer. “Decision Tree Learning on Very Large Data Sets.” Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (SMC), vol. 3, pp. 2579–2584, 1998. PDF
  18. Lawrence O. Hall, Nitesh V. Chawla, and Kevin W. Bowyer. “Combining Decision Trees Learned in Parallel.” Proceedings of the ACM SIGKDD Workshop on Distributed Data Mining (KDDW-DDM), pp. 10–15, 1998. PDF
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