DIAL Publications


  1. David A. Cieslak and Nitesh V. Chawla. “Start Globally, Optimize Locally, Predict Globally: Improving Performance on Unbalanced Data.” Proceedings of the 8th IEEE International Conference on Data Mining (ICDM), pp. 143–152, 2008. PDF
  2. Christopher Moretti, Karsten Steinhaeuser, Douglas L. Thain, and Nitesh V. Chawla. “Scaling Up Classifiers to Cloud Computers.” Proceedings of the 8th IEEE International Conference on Data Mining (ICDM), pp. 472–481, 2008. PDF
  3. Ranga Raju Vatsavai, Olufemi A. Omitaomu, João Gama, Nitesh V. Chawla, Mohamed M. Gaber, and Auroop R. Ganguly. “Knowledge Discovery from Sensor Data (SensorKDD).” ACM SIGKDD Explorations Newletter, 10(2):68–73, 2008. PDF
  4. Darcy A. Davis, Nitesh V. Chawla, Nicholas Blumm, Nicholas A. Christakis, and Albert-László Barabási. “Predicting Individual Disease Risk Based on Medical History.” Proceedings of the 17th ACM Conference on Information and Knowledge Management (CIKM), pp. 769–778, 2008. PDF
  5. David A. Cieslak, Nitesh V. Chawla, and Douglas L. Thain. “Troubleshooting Thousands of Jobs on Production Grids Using Data Mining Techniques.” Proceedings of the 9th IEEE/ACM International Conference on Grid Computing (GRID), pp. 217–224, 2008. PDF
  6. Nitesh V. Chawla, David A. Cieslak, Lawrence O. Hall, and Ajay Joshi. “Automatically Countering Imbalance and Its Empirical Relationship to Cost.” Data Mining and Knowledge Discovery (DMKD), 17(2):225–252, 2008. PDF
  7. David A. Cieslak and Nitesh V. Chawla. “Learning Decision Trees for Unbalanced Data.” Proceedings of the 19th European Conference on Machine Learning and the 12th European Conference on Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD), pp. 241–256, 2008. PDF
  8. Qi Liao, David A. Cieslak, Aaron D. Striegel, and Nitesh V. Chawla. “Using Selective, Short-Term Memory to Improve Resilience Against DDoS Exhaustion Attacks.” Security and Communication Networks, 1(4):287–299, 2008. PDF
  9. Karsten Steinhaeuser and Nitesh V. Chawla. “Is Modularity the Answer to Evaluating Community Structure in Networks?.” Proceedings of the International Workshop and Conference on Network Science (NetSci), 2008. PDF
  10. David A. Cieslak and Nitesh V. Chawla. “Analyzing PETs on Imbalanced Datasets When Training and Testing Class Distributions Differ.” Advances in Knowledge Discovery and Data Mining (PAKDD), pp. 519–526, 2008. PDF
  11. Karsten Steinhaeuser and Nitesh V. Chawla. “Scalable Learning with Thread-Level Parallelism.” Proceedings of the Midwest Artificial Intelligence and Cognitive Science Conference (MAICS), 2008. PDF
  12. Karsten Steinhaeuser and Nitesh V. Chawla. “Community Detection in a Large Real-World Social Network.” Social Computing, Behavioral Modeling, and Prediction, pp. 168–175, 2008. PDF


  1. Alec Pawling, Nitesh V. Chawla, and Gregory Madey. “Anomaly Detection in a Mobile Communication Network.” Computational and Mathematical Organizational Theory, 13(4):407–422, 2007. PDF
  2. David A. Cieslak and Nitesh V. Chawla. “Detecting Fractures in Classifier Performance.” Proceedings of the 7th IEEE International Conference on Data Mining (ICDM), pp. 123–132, 2007. PDF
  3. Vince Thomas, Nitesh V. Chawla, Kevin W. Bowyer, and Patrick J. Flynn. “Learning to Predict Gender from Iris Images.” Proceedings of the 1st IEEE International Conference on Biometrics: Theory, Applications, and Systems (BTAS), pp. 1–5, 2007. PDF
  4. Nitesh V. Chawla and Kevin W. Bowyer. “Actively Exploring Creation of Face Space(s) for Improved Face Recognition.” Proceedings of the National Conference on Artificial Intelligence (AAAI), 22(1):809–814, 2007. PDF
  5. Michael J. Chapple, Nitesh V. Chawla, and Aaron D. Striegel. “Authentication Anomaly Detection: A Case Study on a Virtual Private Network.” Proceedings of the 3rd Annual ACM Workshop on Mining Network Data (MineNet), pp. 17–22, 2007. PDF
  6. Gregory R. Madey, Albert-László Barabási, Nitesh V. Chawla, Marta Gonzales, David Hachen, Brett Lantz, Alec Pawling, Timothy W. Schoenharl, Gábor Szabó, Pu Wang, and Ping Yan. “Enhanced Situational Awareness: Application of DDDAS Concepts to Emergency and Disaster Management.” Proceedings of the 7th International Conference on Computational Science (ICCS), pp. 1090–1097, 2007. PDF
  7. Nitesh V. Chawla and Jared Sylvester. “Exploiting Diversity in Ensembles: Improving Performance on Unbalanced Datasets.” Multiple Classifier Systems (MCS), pp. 397–406, 2007. PDFTanu Malik, Randal Burns, and Nitesh V. Chawla. “A Black-Box Approach to Query Cardinality Estimation.” ACM Conference on Innovative Data Systems Research (CIDR), pp. 56–67, 2007. PDF