Publications

DAR Pages

The Data Analytics Research Project Publications and Technical Reports Note: If there are problems printing the .PDF files, please upgrade to the newest version of Acrobat Reader. 1-norm Support Vector Machines by J. Zhu, S. Rosset, T. Hastie, and R. Tibshirani, in Seventeenth Annual Conference on Neural Information Processing Systems (NIPS), 2003. Margin Maximizing Loss Functions by S. Rosset, J. Zhu, and T. Hastie, in Seventeenth Annual Conference on Neural Information Processing Systems (NIPS), 2003. Integrating Customer Value Considerations into Predictive Modeling by S. Rosset and E. Neumann, in IEEE International Conference on Data Mining (ICDM), 2003. Cost-Sensitive Learning by Cost-Proportionate Example Weighting by B. Zadrozny, J. Langford,N. Abe, in IEEE International Conference on Data Mining (ICDM), 2003. Knowledge-Based Data Mining by S.M. Weiss, S.J. Buckley, S. Kapoor, and S. Damgaard, in Proceedings of the International Conference on Knowledge Discovery and Data Mining, Washington DC, August 24-27, 2003. Passenger-Based Predictive Modeling of Airline No-show Rates by R. D. Lawrence, S.J. Hong, and J. Cherrier, in Proceedings of the International Conference on Knowledge Discovery and Data Mining, Washington DC, August 24-27, 2003. A Machine-Learning Approach to Optimal Bid Pricing by R. D. Lawrence, in Proceedings of the Eighth INFORMS Computing Society Conference on Optimization and Computation in the Network Era, Chandler, Arizona, January 2003. Empirical Comparison of Various Reinforcement Learning Strategies in Sequential Targeted Marketing by N. Abe, E.P.D. Pednault, H. Wang, B. Zadrozny, W. Fan, and C. Apte, in Proceedings of the 2002 IEEE International Conference on Data Mining, December 2002. Prediction of MHC Class I Binding Peptides by Dynamic Experiment Design based on Query Learning with Hidden Markov Models by K. Udaka, H. Mamitsuka, Y. Nakaseko and N. Abe, in Journal of Immunology, 169(10), 5744-5753, 2002. Business Applications of Data Mining, by C. Apte, B. Liu, E.P.D. Pednault, and P. Smyth, in Communications of the ACM, Vol. 45, No. 8, August 2002. A Probabilistic Estimation Framework for Predictive Modeling Analytics, by C. Apte, R. Natarajan, E.P.D. Pednault, and F. Tipu, in IBM Systems Journal, Vol. 41, No. 3, August 2002. Predictive Algorithms in the Management of Computer Systems, by R. Vilalta, C. Apte, J. Hellerstein, S. Ma, and S.M. Weiss, in IBM Systems Journal, Vol. 41, No. 3, August 2002. Automated Generation of Model Cases for Help-Desk Applications, by S.M. Weiss and C. Apte, in IBM Systems Journal, Vol. 41, No. 3, August 2002. Experiments in High-Dimensional Text Categorization, by F. Damerau, T. Zhang, and S.M. Weiss, in Proceedings of ACM SIGIR International Conference on Information Retrieval, August 2002. Sequential Cost-Sensitive Decision Making with Reinforcement Learning, by E.P.D. Pednault, N. Abe, and B. Zadrozny, in Eigth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD), Edmonton, Canada, July 2002. A System for Real-time Competitive Market Intelligence, by S.M. Weiss and N.K. Verma, in Eigth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD), Edmonton, Canada, July 2002. Segmented Regression Estimators for Massive Data Sets by R. Natarajan and E.P.D. Pednault, in Proceedings of the SIAM Second International Conference on Data Mining, Crystal City, Virginia, April 2002. Multiplicative Adjustment of Class Probability: Educating Naive Bayes by S.J. Hong, J. Hosking, R. Natarajan, IBM Research Report RC-22393, April 2002. Condensed version in IEEE ICDM 2002. Personalization of Supermarket Product Recommentations by R. D. Lawrence, G. Almasi, V. Kotlyar, M. Viveros, and S. Duri, in Data Mining and Knowledge Discovery, 5, 11-32, 2001. Segmentation-Based Modeling for Advanced Targeted Marketing, by C. Apte, E. Bibelnieks, R. Natarajan, E.P.D. Pednault, F. Tipu, D. Campbell, and B. Nelson, IBM Research Report RC-21982. In Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD), San Francisco, August 2001. Using Simulated Pseudo Data to Speed Up Statistical Predictive Modeling from Massive Data Sets, by R. Natarajan and E.P.D. Pednault, in SIAM First International Conference on Data Mining, Chicago, IL, April 2001. Solving Regression Problems with Rule-Based Ensemble Classifiers, by N. Indurkhya and S.M. Weiss, in Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD), San Francisco, August 2001. Lightweight Collaborative Filtering Method for Binary-Encoded Data, by S.M. Weiss and N. Indurkhya, in Fifth European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD), Freiburg, Germany, September 2001. A New Approach for Item Choice Recommendations, by S.J. Hong, R. Natarajan, and I. Belitskaya, IBM Research Report RC-21962, in Third International Conference on Data Warehousing and Knowledge Discovery (DaWaK'01), September 2001, Munich, Germany. Active Learning using Adaptive Resampling by V. Iyengar, C. Apte, and T. Zhang, in Proceedings of ACM SIGKDD 2000. Lightweight Rule Induction, by S.M. Weiss and N. Indurkhya, in Proceedings of the International Conference on Machine Learning (ICML) 2000. AI at IBM Research, by C. Apte, L. Morgenstern, and S.J. Hong, in IEEE Intelligent Systems, Nov./Dec. 2000, Volume 15, Number 6, pages 51-57. IBM Research Report RC-21907. Operational Data Analysis: Improved Predictions Using Multi-Computer Pattern Detection, by R. Vilalta, C. Apte, and S.M. Weiss, in Proceedings of the 11th IFIP/IEEE International Workshop on Distributed Systems: Operations & Management (DSOM 2000). Austin, Texas, USA. Lightweight Document Clustering, by S.M. Weiss, B.F. White, and C. Apte, IBM Research Report RC-21684, in Proceedings of PKDD 2000. Decision-Rule Solutions for Data Mining with Missing Values, by S.M. Weiss and N. Indurkhya, IBM Research Report RC-21783, in Proceedings of SBIA/IBERAMIA 2000 (Springer Verlag). Advances in Predictive Models for Data Mining by S.J. Hong and S.M. Weiss. in Pattern Recogntion Letters Journal, 2000, IBM Research Report RC-21570. Earlier version appears in Proceedings of MLDM'99 (Springer), Predictive Data Mining Methods, pp. 13-20. (1999). The Importance of Estimation Errors in Cost Sensitive Learning, by E.P.D. Pednault, B.K. Rosen, and C. Apte, IBM Research Report RC-21757. Handling Imbalanced Data Sets in Insurance Risk Modeling, by E.P.D. Pednault, B.K. Rosen, and C. Apte, IBM Research Report RC-21731. Analysis of Regularized Linear Functions for Classification Problems, by T. Zhang, IBM Research Report RC-21572. Lightweight Document Matching by S.M. Weiss, B.F. White, C. Apte, and F. Damerau, in IJCAI-99 Workshop on Text Mining: Foundations, Applications, and Techniques, 1999. Also in IEEE Intelligent Systems, Volume 15, Number 2, March/April 2000. A Scalable Parallel Algorithm for Self-Organizing Maps with Applications to Sparse Data Mining Problems by R. D. Lawrence, G.S. Almasi, and H. Rushmeier, in Data Mining and Knowledge Discovery, 3, 171-195, 1999. Probabilistic Estimation Based Data Mining for Discovering Insurance Risks by C. Apte, E. Grossman, E. Pednault, B. Rosen, F. Tipu, and B. White. IBM Research Report RC-21483, in IEEE Intelligent Systems, Volume 14, Number 6, November/December 1999. Maximizing Text-Mining Performance by S.M. Weiss, C. Apte, F. Damerau, D.E. Johnson, F.J. Oles, T. Goetz, and T. Hampp, in IEEE Intelligent Systems, Volume 14, Number 4, July/August 1999. Partitioning Nominal Attributes in Decision Trees by D. Coppersmith, S.J. Hong, and J. Hosking, in Journal of Data Mining and Knowledge Discovery, Volume 3, Number 2, June 1999. Earlier version appears in Intelligent Data Engineering and Learning, Proceedings of the 1st International Symposium, IDEAL '98, ed. L. Xu, L. W. Chan, I. King and A. Fu, 393-400. Singapore: Springer-Verlag. IBM Research Division Technical Report RC-21114. Insurance Risk Modeling Using Data Mining Technology by C. Apte, E. Grossman, E. Pednault, B. Rosen, F. Tipu, and B. White, in Proceedings of The Third International Conference on The Practical Applications of Knowledge Discovery and Data Mining, April 1999. IBM Research Division Technical Report RC-21314. Data Mining with Extended Symbolic Models by C. Apte, E. Pednault, and S.M. Weiss, in Proceedings of Joint Statistical Meeting (JSM'98), Statistical Computing Section, 1998. Text Mining with Decision Trees and Decision Rules by C. Apte, F. Damerau, and S.M. Weiss, in Conference on Automated Learning and Discovery, Carnegie-Mellon University, June 1998. Estimating Performance Gains for Voted Decision trees by N. Indurkhya and S.M. Weiss, IBM Research Division Technical Report RC-21199, in Intelligent Data Analysis (IDA). Decomposition of Heterogeneous Classification Problems by C. Apte, S.J. Hong, J. Hosking, J. Lepre, E. Pednault, and B. Rosen, in Intelligent Data Analysis, 1998. Expanded version of paper with same title in proceedings of IDA'97, August 1997. Statistical Learning Theory by E.P.D. Pednault, in the MIT Encyclopedia of the Cognitive Sciences, 1998. Attribute Selection for Modeling by I. Kononenko and S.J. Hong, in Future Generation Computer Systems, November 1997. Data Mining: Guest Editorial by S.J. Hong, in Future Generation Computer Systems, November 1997. Data Mining with Decision Trees and Decision Rules by C. Apte and S.M. Weiss, in Future Generation Computer Systems, November 1997. A Statistical Perspective on Data Mining by J. Hosking, E. Pednault and M. Sudan, in Future Generation Computer Systems, November 1997. Data Mining - An Industrial Research Perspective by C. Apte, in IEEE Computational Science and Engineering, April-June 1997. RAMP: Rules Abstraction for Modeling and Prediction by C. Apte, S.J. Hong, J. Lepre, S. Prasad, and B. Rosen, IBM Research Division Technical Report RC-20271. Use of Randomization to Normalize Feature Merits by S.J. Hong, J. Hosking, and S. Winograd, in proceedings of ISIS'96, 1996. Use of Contextual Information for Feature Ranking and Discretization by S.J. Hong, in IEEE Transactions on Knowledge and Data Engineering, 1997. R-MINI: An Iterative Approach for Generating Minimal Rules from Examples by S.J. Hong, in IEEE Transactions on Knowledge and Data Engineering, 1997. Predicting Equity Returns from Securities Data by C. Apte and S.J. Hong, in Advances in Knowledge Discovery and Data Mining, AAAI Press, 1995. Automated Learning of Decision Rules for Text Categorization by C. Apte, F. Damerau, and S.M. Weiss, in ACM Transactions on Information Systems, 1994. Towards Language Independent Automated Learning of Text Categorization Models by C. Apte, F. Damerau, and S.M. Weiss, in ACM SIGIR'94, July 1994. Case Studies in High-Dimensional Classification by C. Apte, R. Sasisekharan, V. Seshadri, and S.M. Weiss, in Journal of Applied Artificial Intelligence, Vol. 4, No. 3, July 1994. Predicting Defects in Disk Drive Manufacturing: A Case Study in High-Dimensional Classification by C. Apte, S.M. Weiss, and G. Grout, in IEEE Annual Conference on AI Applications, CAIA-93, March 1993. Revised January 26, 2004