2010
| First1 Last1, First2 Last2, ... and Firstn Lastn (Month Year or accepted Month Year, to appear). Reliable Medical Recommendation Systems with Patient Privacy 1st ACM International Health Informatics Symposium (IHI 2010), Publisher (if applicable), vol(nr), start-end (if available). |
| Qi Liao, Aaron Striegel, Nitesh V. Chawla (to appear). Visualizing Dynamics and Similarity in Enterprise Networks. 7th International Symposium on Visualization for Cyber Security (VizSec2010). |
| Ryan Lichtenwalter, Jake Lussier and Nitesh V. Chawla New Perspectives and Methods in Link Prediction ACM SIGKDD Conference on Knowledge Discovery and Data Mining |
| Troy Raeder and Nitesh V. Chawla Market Basket Analysis with Networks Social Networks Analysis and Modeling Journal, in Press |
| Darcy Davis and Nitesh V. Chawla Exploring Disease Interactions Using Combined Gene and Phenotype Networks International Conference on Intelligent Systems for Molecular Biology |
| T. Ryan Hoens and Nitesh V. Chawla (June 2010). Generating Diverse Ensembles to Counter the Problem of Class Imbalance. Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Hyderabad, India. |
| Troy Raeder, Marina Blanton, Nitesh V. Chawla and Keith Frikken (June 2010). Privacy-Preserving Network Aggregation. Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Hyderabad, India. |
| Karsten Steinhaeuser and Nitesh V. Chawla (April 2010). Identifying and Evaluating Community Structure in Complex Networks. Pattern Recognition Letters, 31(5), 413-421. |
| Wei Liu, Sanjay Chawla, David A. Cieslak and Nitesh V. Chawla (April 2010). A Robust Decision Tree Algorithm for Imbalanced Data Sets. SIAM Conference on Data Mining (SDM), Columbus, OH, USA. |
| Jake Lussier, Troy Raeder and Nitesh V. Chawla (March 2010). User Generated Content Consumption and Social Networking in Knowledge-Sharing OSNs. Social Computing, Behavioral Modeling, and Prediction, Springer. |
2009
| Ryan N. Lichtenwalter, Katerina Lichtenwalter and Nitesh V. Chawla (December 2009). Applying Learning Algorithms to Music Generation. Indian International Conference on Artificial Intelligence (IIJCAI), Tumkur, India. |
| James Gray, Darcy Davis, DeWayne Pursley, Jane Smallcomb, Alon Geva and Nitesh V. Chawla (accepted November 2009, to appear). Using Network Analysis of an Electronic Health Record to Examing Team Structure in the Neonatal Intensive Care Unit. Journal of the American Academy of Pediatrics. |
no pdf |
| Lorenzo Beretta, Francesca Cappiello, Alessandro Santaniello, Nitesh V. Chawla, Yannick Allanore, Antonino Mazzone, Francesca Bertolotti and Rafaella Scorza (accepted November 2009, to appear). Development of a Ten-Year Mortality Model in Systemic Sclerosis Patients. Clinical and Experimental Rheumatology. |
no pdf |
| Darcy Davis, Nitesh V. Chawla, Nicholas A. Christakis and Albert László Barabási (accepted November 2009, to appear). Time to CARE: A Collaborative Filtering Engine for Practical Disease Prediction. Data Mining and Knowledge Discovery, doi:10.1007/s10618-009-0156-z. |
| Karsten Steinhaeuser, Nitesh V. Chawla and Auroop R. Ganguly (accepted October 2009, to appear). An Exploration of Climate Data Using Complex Networks. ACM SIGKDD Explorations, 11(2). |
| Faruck Morcos, Charles Lamanna, Nitesh V. Chawla and Jesús Izaguirre (July 2009). Determination of Specificity Residues in Two Component Systems using Graphlets. International Conference on Bioinformatics & Computational Biology (BIOCOMP), Las Vegas, NV, USA. |
| Troy Raeder and Nitesh V. Chawla (July 2009). Model Monitor (M^2): Evaluating, Comparing, and Monitoring Models. Journal of Machine Learning Research (JMLR), 10, 1387-1390. |
| Troy Raeder and 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), Athens, Greece. |
| Karsten Steinhaeuser, Nitesh V. Chawla and Auroop R. Ganguly (June 2009). Discovery of Climate Patterns with Complex Networks. International Workshop and Conference on Network Science (NetSci), Venice, Italy. |
| Karsten Steinhaeuser, Nitesh V. Chawla and Auroop R. Ganguly (June 2009). An Exploration of Climate Data Using Complex Networks. ACM SIGKDD Workshop on Knowledge Discovery from Sensor Data (SensorKDD), Paris, France. |
| Sean McRoskey, Jim Notwell, Nitesh V. Chawla and Christian Poellabauer (June 2009). Mining in a Mobile Environment. ACM SIGKDD Workshop on Knowledge Discovery from Sensor Data (SensorKDD), Paris, France. |
| Ryan Lichtenwalter and Nitesh V. Chawla (accepted April 2009, to appear). Learning to Classify Data Streams with Imbalanced Class Distributions. Proceedings of the PAKDD Conference, LNCS, Springer. |
| Ryan N. Lichtenwalter and Nitesh V. Chawla (April 2009). Adaptive Methods for Classification in Arbitrarily Imbalanced and Drifting Data Streams. PAKDD Workshop for Data Mining When Classes are Imbalanced and Errors have Costs (ICEC), Bangkok, Thailand. |
| 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 (April 2009). Countering Imbalanced Datasets to Improve Adverse Drug Event Predictive Models in Labor and Delivery. Journal of Biomedical Informatics (JBI), 42(2), 356-364. |
| Karsten Steinhaeuser and Nitesh V. Chawla (March 2009). A Network-Based Approach to Understanding and Predicting Diseases. Social Computing, Behavioral Modeling, and Prediction, Springer, 209-216. |
| David A. Cieslak and Nitesh V. Chawla (January 2009). A Framework for Monitoring Classifiers' Performance: When and Why Failure Occurs? Knowledge and Information Systems (KAIS), 18(1), 83-108. |
| Yuchuh Tang, Yan-Qing Zhang, Nitesh V. Chawla and Sven Kresser (February 2009). SVMs Modeling for Highly Imbalanced Classification. IEEE Transactions on Systems, Man and Cybernetics, Part B (SMCB), 39(1), 281-288. |
2008
| David A. Cieslak and Nitesh V. Chawla (December 2008). Start Globally, Optimize Locally, Predict Globally: Improving Performance on Unbalanced Data. IEEE International Conference on Data Mining (ICDM), Pisa, Italy. |
| Christopher Moretti, Karsten Steinhaeuser, Douglas Thain and Nitesh V. Chawla (December 2008). Scaling Up Classifiers to Cloud Computers. IEEE International Conference on Data Mining (ICDM), Pisa, Italy. |
| Raju Vatsavai, Olufemi A. Omitaomu, João Gama, Nitesh V. Chawla, Mohamed M. Gaber and Auroop R. Ganguly (December 2008). Knowledge Discovery from Sensor Data. ACM SIGKDD Explorations, 10(2), 68-73. |
| Darcy Davis, Nitesh V. Chawla, Nicholas Blumm, Nicholas A. Christakis, Albert-László Barabási (October 2008). Predicting Individual Disease Risk Based on Medical History. ACM Conference on Information and Knowledge Management (CIKM), Napa, CA, USA. |
| David A. Cieslak, Nitesh V. Chawla and Douglas Thain (September 2008). Troubleshooting Thousands of Jobs on Production Grids Using Data Mining Techniques. IEEE/ACM International Conference on Grid Computing (GRID), Tsukuba, Japan. |
| David A. Cieslak and Nitesh V. Chawla (September 2008). Learning Decision Trees for Unbalanced Data. European Conference on Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD), Antwerp, Belgium. |
| 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), 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), Osaka, Japan. |
| Karsten Steinhaeuser and Nitesh V. Chawla (June 2008). Is Modularity the Answer to Evaluating Community Structure in Networks? International Workshop and Conference on Network Science (NetSci), Norwich, UK. |
| Karsten Steinhaeuser and Nitesh V. Chawla (April 2008). Scalable Learning with Thread-Level Parallelism. Midwest Artificial Intelligence and Cognitive Science Conference (MAICS), Cincinnati, OH, USA. |
| Karsten Steinhaeuser and Nitesh V. Chawla (March 2008). Community Detection in a Large Real-World Social Network. Social Computing, Behavioral Modeling, and Prediction, Springer, 168-175. |
| Nitesh V. Chawla, David A. Cieslak, Lawrence O. Hall, Ajay Joshi (January 2008). Automatically Countering Imbalance and Its Empirical Relationship to Cost. Data Mining and Knowledge Discovery, 17(2), 225-252. |
2007
| David A. Cieslak and Nitesh V. Chawla (December 2007). Detecting Fracture Points in Classifier Performance. IEEE International Conference on Data Mining (ICDM), Omaha, NE, USA. |
| Alec Pawling, Nitesh V. Chawla and Gregory Madey (December 2007). Anomaly Detection in Mobile Communication Networks. Computational and Mathematical Organizational Theory, 13(4), 407-422. |
| Vince Thomas, Nitesh V. Chawla, Kevin W. Bowyer and Patrick J. Flynn (September 2007). Learning to Predict Gender from Iris Images. IEEE Conference on Biometrics: Theory, Applications and Systems (BTAS), Washington, DC, USA. |
| Nitesh V. Chawla and Kevin W. Bowyer (July 2007). Actively Exploring Creation of Face Spaces for Improved Face Recognition. Conference on Artificial Intelligence (AAAI), Vancouver, Canada. |
| Michael J. Chapple, Nitesh V. Chawla and Aaron Striegel (June 2007). Authentication Anomaly Detection: A Case Study on a Virtual Private Network. ACM SIGMETRICS Workshop on Mining Network Data (MineNet), San Diego, CA, USA. |
| Gregory R. Madey, Albert-László Barabási, Nitesh V. Chawla, Marta Gonzales,
David Hachen, Brett Lantz, Alec Pawling, Timothy W. Schoenharl, Gábor Szabó, Pu Wang, Ping Yan (May 2007). Enhanced Situational Awareness: Application of DDDAS Concepts to Emergency and Disaster Management. International Conference on Computational Science (ICCS), Beijing, China. |
| Nitesh V. Chawla and Jared Sylvester (May 2007). Exploiting Diversity in Ensembles: Improving Performance on Unbalanced Datasets. International Workshop on Multiple Classifier Systems (MCS), Prague, Czech Republic. |
| Tanu Malik, Randal Burns and Nitesh V. Chawla (January 2007). A Black-Box Approach to Query Cardinality Estimation. ACM Conference on Innovative Data Systems Research (CIDM). |
2006
| Tanu Malik, Randal Burns, Nitesh V. Chawla and Alexander S. Szalay (November 2006). Estimating Query Result Sizes for Proxy Caching in Scientific Database Federations. ACM/IEEE Supercomputing, Tampa, FL. |
| Yang Liu, Nitesh V. Chawla, Mary P. Harper, Elizabeth Shriberg and Andreas Stolcke (October 2006). A Study in Machine Learning from Imbalanced Data for Sentence Boundary Detection in Speech. Journal of Computer Speech and Language, 20(4), 468-494. |
| Karsten Steinhaeuser, Nitesh V. Chawla and Peter M. Kogge (September 2006). Exploiting Thread-Level Parallelism to Build Decision Trees. ECML/PKDD Workshop on Parallel and Distributed Data Mining, Berlin, Germany. |
| Dinesh Rajan, Christian Poellabauer and Nitesh V. Chawla (September 2006). Resource Access Pattern Mining for Dynamic Energy Management. ECML/PKDD Workshop on Automatic Computing: A New Challenge for Machine Learning, Berlin, Germany. |
| Alec Pawling, Nitesh V. Chawla and Amitabh Chaudhary (September 2006). Evaluation of Summarization Schemes for Learning in Streams. European Conference on Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD), Berlin, Germany. |
| Nitesh V. Chawla and Xiangning Li (August 2006). Pricing Scheme for Benefit Scoring. ACM SIGKDD Workshop on Utility Based Data Mining (UBDM), Philadelphia, PA, USA. |
| Danny Roobaert, Grigoris Karakoulas and Nitesh V. Chawla (August 2006). Information Gain, Correlation and Support Vector Machines. Feature Extraction: Foundations and Applications, Springer, 463-470. |
| Nitesh V. Chawla and David A. Cieslak (July 2006). Evaluating Probability Estimates from Decision Trees. AAAI Workshop on Evaluation Methods for Machine Learning, Boston, MA, USA. |
| Jared Sylvester and Nitesh V. Chawla (July 2006). Evolutionary Ensemble Creation and Thinning. IEEE International Joint Conference on Neural Networks (IJCNN), Vancouver, Canada. |
| Nitesh V. Chawla (July 2006). Many Are Better Than One: Improving Probabilistic Estimates from Decision Trees. Machine Learning Challenges, Springer, 41-55. |
| Karsten Steinhaeuser, Nitesh V. Chawla and Christian Poellabauer (June 2006). Towards Learning-Based Sensor Management. First Workshop on Tackling Computer Systems Problems with Machine Learning (SysML), Saint-Malo, France. |
| David A. Cieslak, Douglas Thain and Nitesh V. Chawla (June 2006). Troubleshooting Distributed Systems via Data Mining. IEEE International Symposium on High Performance Distributed Computing (HPDC), Paris, France. |
| Alec Pawling, Nitesh V. Chawla and Gregory Madey (June 2006). Anomaly Detection in a Mobile Communication Network. Annual Conference of the North American Association for Computational Social and Organizational Science (NAACSOS), Notre Dame, IN, USA. |
| David A. Cieslak, Nitesh V. Chawla and Aaron Striegel (May 2006). Combating Imbalance in Network Intrusion Data. IEEE International Conference on Granular Computing (GrC), Atlanta, GA, USA. |
2005
| Alec Pawling, Nitesh V. Chawla and Amitabh Chaudhary (November 2005). Computing Information Gain in Data Streams. IEEE ICDM Workshop on Temporal Data Mining, Houston, TX, USA. |
| Nitesh V. Chawla and Kevin W. Bowyer (October 2005). Ensembles in Face Recognition: Tackling the Extremes of High Dimensionality, Temporality, and Variance in Data. IEEE International Conference on Systems, Man and Cybernetics (SMC), Big Island, Hawaii, USA. |
| Nitesh V. Chawla (September 2005). Data Mining for Imbalanced Datasets: An Overview. Data Mining and Knowledge Discovery Handbook: A Complete Guide for Practitioners and Researchers, Springer, 853-867. |
| Nitesh V. Chawla, Lawrence O. Hall and Ajay Joshi (August 2005). Wrapper-Based Computation and Evaluation of Sampling Methods for Imbalanced Datasets. ACM SIGKDD Workshop on Utility-Based Data Mining (UBDM), Chicago, IL, USA. |
| Jared Sylvester and Nitesh V. Chawla (July 2005). Evolutionary Ensembles: Combining Learning Agents Using Genetic Algorithms. AAAI Workshop on Multi-Agent Systems, Pittsburgh, PA, USA. |
| Nitesh V. Chawla and Kevin W. Bowyer (June 2005). Random Subspaces and Subsampling for 2-D Face Recognition. Computer Vision and Pattern Recognition, 2, 582-589. |
| Nitesh V. Chawla (June 2005). Teaching Data Mining by Coalescing Theory and Applications. International Conference on Frontiers in Education, Las Vegas, NV, USA. |
| Nitesh V. Chawla and Kevin W. Bowyer (June 2005). Designing Multiple Classifier Systems for Face Recognition. International Workshop on Multiple Classifier Systems (MCS), Seaside, CA, USA. |
| Daniel Mack, Nitesh V. Chawla and Gregory Madey (June 2005). Activity Mining in Open Source Software. Annual Conference of the North American Association for Computational Social and Organizational Science (NAACSOS), Notre Dame, IN, USA. |
| 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, 23, 331-366. |
2004
| Nitesh V. Chawla, Lawrence O. Hall, Kevin W. Bowyer and W. Philip Kegelmeyer (December 2004). Learning Ensembles from Bites: A Scalable and Accurate Approach. Journal of Machine Learning Research, 5, 421-451. |
| Steven Eschrich, Nitesh V. Chawla and Lawrence O. Hall (November 2004). Learning to Predict in Complex Biological Domains. Journal of System Simulation, 14(11), 1464-1471. |
no pdf |
| Predrag Radivojac, Nitesh V. Chawla, A. Keith Dunker and Zoran Obradovic (August 2004). Classification and Knowledge Discovery in Protein Databases. Journal of Biomedical Informatics, 37(4), 224-239. |
| Nitesh V. Chawla, Nathalie Japkowicz and Aleksander Kolcz (June 2004). Learning From Imbalanced Datasets. ACM SIGKDD Explorations, 6(1), 1-6. |
2003
| Nitesh V. Chawla, Grigoris Karakoulas and Danny Roobaert (December 2003). Lessons Learned from the NIPS Feature Selection Challenge. NIPS Workshop on Feature Selection, Vancouver, Canada. |
| Nitesh V. Chawla, Aleksandar Lazarevic, Lawrence O. Hall and Kevin W. Bowyer (September 2003) SMOTEBoost: Improving the Prediction of the Minority Class in Boosting. European Conference on Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD), Cavtat-Dubrovnik, Croatia. |
| Nitesh V. Chawla (August 2003). C4.5 and Imbalanced Data Sets: Investigating the Effect of Sampling Method, Probabilistic Estimate, and Decision Tree Structure. ICML Workshop on Learning from Imbalanced Data Sets II, Washington, DC, USA. |
| Nitesh V. Chawla, Thomas E. Moore, Lawrence O. Hall, Kevin W. Bowyer, W. Philip Kegelmeyer and Clayton Springer (January 2003). Distributed Learning with Bagging-Like Performance. Pattern Recognition Letters, 24(1),455-471. |
2002
| Steven Eschrich, Nitesh V. Chawla and Lawrence O. Hall (July 2002). Generalization Methods in Bioinformatics. ACM SIGKDD Workshop on Data Mining in Bioinformatics (BIOKDD), Edmonton, Canada. |
| Nitesh V. Chawla, Kevin W. Bowyer, Thomas E. Moore and Philip Kegelmeyer (June 2002). SMOTE: Synthetic Minority Over-Sampling Technique. Journal of Artificial Intelligence Research, 16, 321-357. |
| Nitesh V. Chawla, Lawrence O. Hall, Kevin W. Bowyer, Thomas E. Moore, W. Philip Kegelmeyer (June 2002). Distributed Pasting of Small Votes. International Workshop on Multiple Classifier Systems (MCS), Cagliari, Italy. |
2001
| Nitesh V. Chawla, Steven Eschrich and Lawrence O. Hall (November 2001). Creating Ensembles of Classifiers. IEEE International Conference on Data Mining (ICDM), San Jose, CA, USA. |
| Nitesh V. Chawla, Thomas E. Moore, Kevin W. Bowyer, Lawrence O. Hall, Clayton Springer and W. Philip Kegelmeyer (August 2001). Investigation of Bagging-Like Effects and Decision Trees Versus Neural Nets in Protein Secondary Structure Prediction. ACM SIGKDD Workshop on Data Mining in Bioinformatics (BIOKDD), San Francisco, CA, USA. |
| Nitesh V. Chawla, Thomas E. Moore, Kevin W. Bowyer, Lawrence O. Hall, Clayton Springer and W. Philip Kegelmeyer (August 2001). Bagging is a Small-Data-Set Phenomenon. Computer Vision and Pattern Recognition, 2, 684-689. |
2000
| Kevin W. Bowyer, Lawrence O. Hall, Thomas E. Moore, Nitesh V. Chawla and W. Phillip Kegelmeyer (October 2000). A Parallel Decision Tree Builder for Mining Very Large Visualization Datasets. IEEE International Conference on Systems, Man and Cybernetics (SMC), Nashville, TN, USA. |
| Lawrence O. Hall, Nitesh V. Chawla, Kevin W. Bowyer and W. Philip Kegelmeyer (January 2000). Learning Rules from Distributed Data. Large-Scale Parallel Data Mining, LNAI, Springer, 211-220. |
1999
| Lawrence O. Hall, Nitesh V. Chawla, Kevin W. Bowyer and W. Philip Kegelmeyer (August 1999). Learning Rules from Distributed Data. ACM SIGKDD Workshop on Large-Scale Parallel Data Mining, San Diego, CA, USA. |
1998
| Lawrence O. Hall, Nitesh V. Chawla and Kevin W. Bowyer (August 1998). Combining Decision Trees Learned in Parallel. ACM SIGKDD Workshop on Distributed Data Mining, New York, NY, USA. |
| Lawrence O. Hall, Nitesh V. Chawla and Kevin W. Bowyer (July 1998). Decision Tree Learning on Very Large Data Sets. IEEE International Conference on Systems, Man and Cybernetics (SMC), San Diego, CA, USA. |