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DIAL Publications

2009

  1. Ryan N. Lichtenwalter, Katerina Lichtenwalter, and Nitesh V. Chawla. “Applying Learning Algorithms to Music Generation. ” Proceedings of the 4th Indian International Conference on Artificial Intelligence (IICAI), pp. 483–502, 2009. PDF
  2. Olufemi A. Omitaomu, Auroop R. Ganguly, João Gama, Ranga Raju Vatsavai, Nitesh V. Chawla, and Mohamed M. Gaber. “Knowledge Discovery from Sensor Data (SensorKDD).” ACM SIGKDD Explorations Newsletter, 11(2):84–87, 2009. PDF
  3. Faruck Morcos, Charles Lamanna, Nitesh V. Chawla, and Jesús Izaguirre. “Determination of Specificity Residues in Two Component Systems Using Graphlets.” Proceedings of the International Conference on Bioinformatics & Computational Biology (BIOCOMP), pp. 860–866, 2009. PDF
  4. Troy Raeder and Nitesh V. Chawla. “Model Monitor (M2): Evaluating, Comparing, and Monitoring Models.” Journal of Machine Learning Research (JMLR), 10(Jul):1387–1390, 2009. PDF
  5. Troy Raeder and Nitesh V. Chawla. “Modeling a Store’s Product Space as a Social Network.” Proceedings of the IEEE/ACM International Conference on Advances in Social Network Analysis and Mining (ASONAM), pp. 164–169, 2009. PDF
  6. Karsten Steinhaeuser, Nitesh V. Chawla, and Auroop R. Ganguly. “An Exploration of Climate Data Using Complex Networks.” Proceedings of the 3rd ACM SIGKDD International Workshop on Knowledge Discovery from Sensor Data (SensorKDD), pp. 23–31, 2009. PDF
  7. Sean McRoskey, James Notwell, Nitesh V. Chawla, and Christian Poellabauer. “Mining in a Mobile Environment.” Proceedings of the 3rd ACM SIGKDD International Workshop on Knowledge Discovery from Sensor Data (SensorKDD), pp 56–60, 2009. PDF
  8. Ryan N. Lichtenwalter and Nitesh V. Chawla. “Learning to Classify Data Streams with Imbalanced Class Distributions.” Proceedings of the Pacific Asia Knowledge Discovery and Data Mining Conference (PAKDD), 2009. PDF
  9. Ryan N. Lichtenwalter and Nitesh V. Chawla. “Adaptive Methods for Classification in Arbitrarily Imbalanced and Drifting Data Streams.” Proceedings of the PAKDD Workshop on Data Mining When Classes are Imbalanced and Errors Have Costs (PAKDD-ICEC), pp. 53–75, 2009. PDF
  10. Laritza M. Taft, R. Scott Evans, Chi-Ren Shyu, Marlene J. Egger, Nitesh V. Chawla, Joyce A. Mitchell, Sidney N. Thornton, Bruce Bray, and Michael W. Varner. “Countering Imbalanced Datasets to Improve Adverse Drug Event Predictive Models in Labor and Delivery.” Journal of Biomedical Informatics (JBI), 42(2):356–364, 2009. PDF
  11. Karsten Steinhaeuser and Nitesh V. Chawla. “A Network-Based Approach to Understanding and Predicting Diseases.” Social Computing and Behavioral Modeling, pp. 1–8, 2009. PDF
  12. Yuchuh Tang, Yan-Qing Zhang, Nitesh V. Chawla, and Sven Kresser. “SVMs Modeling for Highly Imbalanced Classification.” IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics, 39(1):281–288, 2009. PDF
  13. David A. Cieslak and Nitesh V. Chawla. “A Framework for Monitoring Classifiers’ Performance: When and Why Failure Occurs?.” Knowledge and Information Systems (KAIS), 18(1):83–108, 2009. PDF

2008

  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
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