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DIAL Publications

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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