2009
| Ryan Lichtenwalter, Katerina Lichtenwalter and Nitesh V. Chawla (2009).
Applying Learning Algorithms to Music Generation Indian International Conference on Artificial Intelligence |
| Faruck Morcos, Charles Lamanna,
Nitesh V. Chawla, and Jesus A. Izaguirre(2009).
Determination of Specificity Residues in Two Component Systems using Graphlets International Conference on Bioinformatics & Computational Biology |
| Troy Raeder, Nitesh V. Chawla (July 2009). Model Monitor (M^2): Evaluating, Comparing, and Monitoring Models Journal of Machine Learning Research (JMLR), 10:1387--1390, 2009 |
| Troy Raeder, Nitesh V. Chawla (July 2009). Modeling a Store's Product Space as a Social Network ACM/IEEE Conference on Advances in Social Network Analysis and Mining (ASONAM). |
| Karsten Steinhaeuser, Nitesh V. Chawla, and Auroop Ganguly (June 2009). An Exploration of Climate Data Using Complex Networks. ACM SIGKDD Workshop on Knowledge Discovery from Sensor Data (SensorKDD). |
no pdf |
| Sean McRoskey, Jim Notwell, Nitesh V. Chawla, and Christian Poellabauers(June
2009). Mining in a Mobile Environment ACM SIGKDD Workshop on Knowledge Discovery from Sensor Data (SensorKDD). |
no pdf |
| Ryan N. Lichtenwalter and Nitesh V. Chawla (April 2009). Adaptive Methods for Classification in Arbitrarily Imbalanced and Drifting Data Streams. Pacific-Asia Conference on Knowledge Discovery and Data Mining Workshop for Data Mining When Classes are Imbalanced and Errors Have Costs (ICEC)., pg. 86-97. |
no pdf |
| L. M. taft, R. S. Evans, C. R. Shyu, M. J. Egger, N. V.
Chawla, J. A. Mitchell, S.N. Thornton, B. Bray, and M. Varner
(2009). Countering imbalanced datasets to improve adverse drug event predictive models in labor and delivery Journal of Biomedical Informatics (JBI), 42(2), pg. 356 -- 364. |
| David A. Cieslak and Nitesh V. Chawla (2009). A Framework for Monitoring Classifiers' Performance: When and Why Failure Occurs? Knowledge and Information Systems (KAIS), 18(1), pg. 83-108. |
| Yuchuh Tang, Yan-Qing Zhang, Nitesh V. Chawla, and Sven Kresser (2009). SVMs Modeling for Highly Imbalanced Classification IEEE Transactions on Systems Man and Cybernetics (SMCB), 39 (1), pg 281-288. |
2008
| David A. Cieslak, Nitesh V. Chawla (December 2008). Start Globally, Optimize Locally, Predict Globally: Improving Performance on Unbalanced Data IEEE International Conference on Data Mining (ICDM), pg. 143-152. |
| Christopher Moretti, Karsten Steinhaeuser, Douglas Thain, Nitesh V. Chawla (December 2008). Scaling Up Classifiers to Cloud Computers IEEE International Conference on Data Mining (ICDM), pg. 472-481. |
| Darcy Davis, Nitesh V. Chawla, Nicholas Blumm, Nicholas Christakis, Albert-Laszlo Barabasi (October 2008). Predicting individual disease risk based on medical history ACM Conference on Information and Knowledge Management (CIKM)i, pg. 769-778. |
| David A. Cieslak, Nitesh V. Chawla, and Douglas Thain (October 2008). Troubleshooting Thousands of Jobs on Production Grids Using Data Mining Techniques IEEE/ACM International Conference on Grid Computing (GRID), pg. 217-224. |
| David A. Cieslak and Nitesh V. Chawla (September 2008). Learning Decision Trees for Unbalanced Data European Conference on Machine Learning (ECML), pg. 241-256. |
| Qi Liao, David A. Cieslak, Aaron D. Striegel, and Nitesh V. Chawla (June 2008). Using selective, short-term memory to improve resilience against DDoS exhaustion attacks Security and Communication Networks, 1(4), pg. 287-299. |
| David A. Cieslak and Nitesh V. Chawla (May 2008). Analyzing Classifier Performance on Imbalanced Datasets when Training and Testing Distributions Differ Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), pg. 519-526. |
| Karsten Steinhaeuser and Nitesh V. Chawla (2008). Is Modularity the Answer to Evaluating Community Structure in Networks? International Workshop and Conference on Network Science (NetSci). |
| Karsten Steinhaeuser and Nitesh V. Chawla (2008). Scalable Learning with Thread-Level Parallelism Midwest Artificial Intelligence and Cognitive Science Conference (MAICS). |
| Karsten Steinhaeuser and Nitesh V. Chawla (2008). Community Detection in a Large Real-World Social Network. Social Computing, Behavioral Modeling and Prediction, H. Liu, J.J. Salerno, M.J. Young (Eds.), Springer Verlag. |
| Nitesh V. Chawla, David A. Cieslak, Larry Hall, and Ajay Joshi (2008). Automatically Countering Imbalance and Its Empirical Relationship to Cost Data Mining and Knowledge Discovery (DMKD), 17(2), pg. 225-252. |
2007
| David A. Cieslak and Nitesh V. Chawla (2007). Detecting Fracture Points in Classifier Performance. 7th IEEE International Conference on Data Mining (ICDM). |
| Vince Thomas, Nitesh V. Chawla, Kevin Bowyer, and Pat Flynn (September 2007). Learning to Predict Gender from Iris Images. 1st IEEE Conference on Biometrics: Theory, Applications and Systems (BTAS), pg. 1-5. |
| Nitesh V. Chawla and Kevin Bowyer (July 2007). Actively Exploring Creation of Face Spaces for Improved Face Recognition 22nd National Conference on Artificial Intelligence (AAAI), pg. 809-814. |
| Michael J. Chapple, Nitesh V. Chawla, Aaron Striegel (June 2007). Authentication anomaly detection: a case study on a virtual private network MineNet 2007, pg. 17-22. |
| Gregory R. Madey, Albert-Läszlö Barabäsi, Nitesh V. Chawla, Marta Gonzalez, David Hachen, Brett Lantz, Alec Pawling, Timothy W. Schoenharl, Gäbor Szabö, Pu Wang, Ping Yan (May 2007). Enhanced Situational Awareness: Application of DDDAS Concepts to Emergency and Disaster Management International Conference on Computational Science (ICCS), pg. 1090 - 1097. |
| Nitesh V. Chawla and Jared Sylvester (May 2007). Exploiting Diversity in Ensembles: Improving Performance on Unbalanced Datasets 7th International Workshop on Multiple Classifier Systems (MCS), pg. 397-406. |
2006
| Karsten Steinhaeuser, Nitesh V. Chawla and Peter Kogge (2006). Exploiting Thread-level Parallelism to Build Decision Trees Workshop on Parallel and Distributed Data Mining(ECML/PKDD). |
| Nitesh V. Chawla and Xiangning Li (2006). Pricing Scheme for Benefit Scoring 2nd Workshop on Utility Based Data Mining (UBDM/KDD). |
| Nitesh V. Chawla and David A. Cieslak (July 2006). Evaluating Calibration of Probability Estimation Trees AAAI Workshop on the Evaluation Methods in Machine Learnin Workshop on Utility Based Data Mining (AAAI), pg. 18-23. |
| Karsten Steinhaeuser, Nitesh V. Chawla, and Christian Poellabauer (2006). Towards Learning-based Sensor Management First Workshop on Tackling Computer Systems Problems with Machine Learning Techniques (SYSML). |
| David A. Cieslak, Douglas Thain and Nitesh V. Chawla (June 2006). Troubleshooting Distributed Systems via Data Mining 15th IEEE International Symposium on High Performance Distributed Computing (HPDC-15), pg. 309-312. |
| Jared Sylvester and Nitesh V. Chawla (July 2006). Evolutionary Ensemble Creation and Thinning IEEE International Joint Conference on Neural Networks (IJCNN), pg. 5148-5155. |
| David A. Cieslak, Nitesh V. Chawla and Aaron Striegel (May 2006). Combating Imbalance in Network Intrusion Data IEEE International Conference on Granular Computing (GrC), pg. 732-737. |
2005
| Nitesh V. Chawla and Kevin W. Bowyer (June 2005). Random Subspaces and Subsampling for 2-D Face Recognition Computer Vision and Pattern Recognition, pg. 582-589. |
| Nitesh V. Chawla and Grigoris J. Karakoulas (March 2005). Learning From Labeled And Unlabeled Data: An Empirical Study Across Techniques And Domains Journal of Artificial Intelligence Research, pg. 331-366. |
2004
| Predrag Radivojac, Nitesh V. Chawla, A. Keith Dunker and Zoran Obradovic (Aug 2004). Classification and Knowledge Discovery in Protein Databases Journal of Biomedical Informatics, pg. 224-239. |
| Nitesh V. Chawla, Lawrence O. Hall, Kevin W. Bowyer and W. Philip Kegelmeyer (Apr 2004). Learning Ensembles from Bites: A Scalable and Accurate Approach Journal of Machine Learning Research, pg. 421-451. |
2003
| Nitesh V. Chawla, Thomas E. Moore, Lawrence O. Hall, Kevin W. Bowyer, W. Philip Kegelmeyer and Clayton Springer (2003). Distributed Learning with Bagging-like Performance Pattern Recognition Letters, pg. 455-471. |
2002
| Steven Eschrich, Nitesh V. Chawla and Lawrence O. Hall (2002). Generalization Methods in Bioinformatics BIOKDD, pg. 25-32. |
| Nitesh V. Chawla, Kevin W. Bowyer, Thomas E. Moore and W. Philip Kegelmeyer (June 2002). SMOTE: Synthetic Minority Over-sampling Technique Journal of Artificial Intelligence Research, pg. 321-357. |
2001
| Nitesh V. Chawla, Thomas E. Moore, Kevin W. Bowyer, Lawrence O. Hall, Clayton Springer and W. Philip Kegelmeyer (2001). Investigation of Bagging-like Effects and Decision Trees Versus Neural Nets in Protein Secondary Structure Prediction BIOKDD, pg. 50-59. |
| Nitesh V. Chawla, Thomas E. Moore, Kevin W. Bowyer, Lawrence O. Hall, Clayton Springer and W. Philip Kegelmeyer (2001). Bagging Is a Small-Data-Set Phenomenon Computer Vision and Pattern recognition, pg. 684-689. |
| Nitesh V. Chawla, Steven Eschrich and Lawrence O. Hall (2001). Creating Ensembles of Classifiers First IEEE International Conference on Data Mining, pg. 580-581. |
1999
| Lawrence O. Hall, Nitesh V. Chawla, Kevin W. Bowyer and W. Philip Kegelmeyer (1999). Learning Rules from Distributed Data Learning Large-Scale Parallel data Mining, pg. 211-220. |