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DIAL Publications

2010

  1. Andrew K. Rider, Geoffrey H. Siwo, Nitesh V. Chawla, Michael T. Ferdig, and Scott J. Emrich. “A Supervised Learning Approach to the Unsupervised Clustering of Genes.” Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 322–328, 2010. PDF
  2. Troy Raeder, T. Ryan Hoens, and Nitesh V. Chawla. “Consequences of Variability in Classifier Performance Estimates.” Proceedings of the 10th IEEE International Conference on Data Mining (ICDM), pp. 421–430, 2010. PDF
  3. Karsten Steinhaeuser, Nitesh V. Chawla, and Auroop R. Ganguly. “Complex Networks as a Unified Framework for Descriptive Analysis and Predictive Modeling in Climate Science.” Statistical Analysis and Data Mining (SADM), 4(5):497–511, 2010. PDF
  4. T. Ryan Hoens, Marina Blanton, and Nitesh V. Chawla. “Reliable Medical Recommendation Systems with Patient Privacy.” Proceedings of the 1st ACM International Health Informatics Symposium (IHI), pp. 173–182, 2010. PDF
  5. Karsten Steinhaeuser, Nitesh V. Chawla, and Auroop R. Ganguly. “An Exploration of Climate Data Using Complex Networks.” ACM SIGKDD Explorations Newletter, 12(1):25–32, 2010. PDF
  6. Karsten Steinhaeuser, Nitesh V. Chawla, and Auroop R. Ganguly. “Complex Networks in Climate Science: Progress, Opportunities and Challenges.” Proceedings of the IEEE Conference on Intelligent Data Understanding (CIDU), pp. 16–26, 2010. PDF
  7. Andrew K. Rider, Geoffrey H. Siwo, Nitesh V. Chawla, Michael T. Ferdig, and Scott J. Emrich. “A Statistical Approach to Finding Overlooked Genetic Associations.” BMC Bioinformatics, 11:526, 2010. PDF
  8. Qi Liao, Aaron D. Striegel, and Nitesh V. Chawla. “Visualizing Graph Dynamics and Similarity for Enterprise Network Security and Management.” Proceedings of the 7th ACM International Symposium on Visualization for Cyber Security (VizSec), pp. 34–45, 2010. PDF
  9. T. Ryan Hoens, Marina Blanton, and Nitesh V. Chawla. “A Private and Reliable Recommendation System for Social Networks.” Proceedings of the 2nd IEEE International Conference on Social Computing (SocialCom), pp. 816–825, 2010. PDF
  10. Gregory Ditzler, Nitesh V. Chawla, and Robi Polikar. “An Incremental Learning Algorithm for Nonstationary Environments and Class Imbalance.” Proceedings of the 20th International Conference on Pattern Recognition (ICPR), pp. 2997–3000, 2010. PDF
  11. Ryan N. Lichtenwalter, Jake T. Lussier, and Nitesh V. Chawla. “New Perspectives and Methods in Link Prediction.” Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp. 243–252, 2010. PDF
  12. T. Ryan Hoens and Nitesh V. Chawla. “Generating Diverse Ensembles to Counter the Problem of Class Imbalance.” Advances in Knowledge Discovery and Data Mining (PAKDD), pp. 488–499, 2010. PDF
  13. Troy Raeder, Marina Blanton, Nitesh V. Chawla, and Keith Frikken. “Privacy-Preserving Network Aggregation.” Advances in Knowledge Discovery and Data Mining (PAKDD), pp. 198–207, 2010. PDF
  14. James E. Gray, Darcy A. Davis, DeWayne M. Pursley, Jane E. Smallcomb, Alon Geva, and Nitesh V. Chawla. “Network Analysis of Team Structure in the Neonatal Intensive Care Unit.” Journal of the American Academy of Pediatrics, 125(6):e1460–e1467, 2010. PDF
  15. Darcy A. Davis, Nitesh V. Chawla, Nicholas A. Christakis, and Albert László Barabási. “Time to CARE: A Collaborative Filtering Engine for Practical Disease Prediction.” Data Mining and Knowledge Discovery (DMKD), 20(3):388–415, 2010. PDF
  16. Karsten Steinhaeuser and Nitesh V. Chawla. “Identifying and Evaluating Community Structure in Complex Networks.” Pattern Recognition Letters, 31(5):413–421, 2010. PDF
  17. Lorenzo Beretta, Alessandro Santaniello, Francesca Cappiello, Nitesh V. Chawla, Madelon C. Vonk, Patricia E. Carreira, Yannick Allanore, Delia A. Popa-Diaconu, Marta Cossu, Francesca Bertolotti, Gianfranco Ferraccioli, Antonino Mazzone, and Rafaella Scorza. “Development of a Five-Year Mortality Model in Systemic Sclerosis Patients by Different Analytical Approaches.” Clinical & Experimental Rheumatology (CER), 28(2):S18–27, 2010. PDF
  18. Jake T. Lussier, Troy Raeder, and Nitesh V. Chawla. “User Generated Content Consumption and Social Networking in Knowledge-Sharing OSNs.” Advances in Social Computing, pp. 228–237, 2010. PDF
  19. Wei Liu, Sanjay Chawla, David A. Cieslak, and Nitesh V. Chawla. “A Robust Decision Tree Algorithm for Imbalanced Data Sets.” Proceedings of the SIAM Conference on Data Mining (SDM), 766–777, 2010. PDF

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
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