
			The Data Analytics Research Project

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Data Management
Knowledge Discovery
& Data Mining

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Publications

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2005

Embedded Predictive Modeling in a Parallel Relational Database by A. Dorneich, R. Natarajan, E. Pednault and C. Apte, to appear in Proceedings of the 21st ACM Symposium on Applied Computing, Special Track on Data Mining, April 2006, Dijon, France.

Data Mining and Clinical Data Repositories: Insights from a 667,000 Patient Data Set by B. Robson, C. Apte, S. Weiss et al., to appear in Computers in Biology and Medicine, 2005.

Ranking-Based Evaluation of Regression Models by S. Rosset, C. Perlich and B. Zadrozny, in Proceedings of the Fifth IEEE International Conference on Data Mining, November 2005.

An Improved Categorization of Classifier's Sensitivity on Sample Selection Bias by W. Fan, I. Davidson, B. Zadrozny, and P. S. Yu, in Proceedings of the Fifth IEEE International Conference on Data Mining, November 2005.

Business Performance Management System for CRM and Sales Execution by M. Ettl, B. Zadrozny, P. Chowdhary and N. Abe, in Proceedings of the Sixteenth International Conference on Database and Expert Systems Applications, pp. 908-913, August 2005.

Robust Boosting and Its Relation to Bagging by S. Rosset, in Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2005.

Gene Classification: Issues and Challenges for Relational Learning by C. Perlich and S. Merugu, in Proceedings of the Workshop on Multi-Relational Data Mining (MRDM), at the Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2005.

One-Benefit Learning: Cost-Sensitive Learning with Restricted Cost Information by B. Zadrozny, in Proceedings of the Workshop on Utility-Based Data Mining (UBDM), at the Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2005.

ROC Confidence Bands: An Empirical Evaluation by S. Macskassy, F. Provost and S. Rosset, in Proceedings of the Twenty-Second International Conference on Machine Learning, August 2005.

Error Limiting Reductions Between Classification Tasks by A. Beygelzimer, V. Dani, T. Hayes, J. Langford and B. Zadrozny, in Proceedings of the Twenty-Second International Conference on Machine Learning, August 2005.

Relating Reinforcement Learning Performance to Classification Performance by J. Langford and B. Zadrozny, in Proceedings of the Twenty-Second International Conference on Machine Learning, August 2005.

Approaching the ILP Challenge 2005: Class-Conditional Bayesian Propositionalization for Genetic Classification by C. Perlich, in Proceedings of the Fifteenth International Conference on Inductive Logic Programming, August 2005.

Weighted One-Against-All by A. Beygelzimer, J. Langford and B. Zadrozny, in Proceedings of the Twentieth National Conference on Artificial Intelligence, July 2005.

Learning from Identifier Attributes: Distribution-Based Aggregation for Relational Learning by C. Perlich and F. Provost, in Proceedings of the Dagstuhl Seminar 05051 (Probabilistic, Logical and Relational Learning - Towards a Synthesis), February 2005.

Sparsity and Smoothness via the Fused Lasso by R. Tibshirani, M. Saunders, S. Rosset, J. Zhu and Keith Knight, in Journal of the Royal Statistical Society Series B, Vol. 67 No. 1, 2005.

Estimating Class Membership Probabilities using Classifier Learners by J. Langford and B. Zadrozny, in Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics, January 2005.

Improvements to the Linear Programming based Scheduling of Web Advertisements by A. Nakamura and N. Abe, in Journal of Electronic Commerce Research, 5(1), 75-98, 2005.

Sequential Risk Management in E-Business by Reinforcement Learning, by N. Abe, E. Pednault, B. Zadrozny, H. Wang, W. Fan, and C. Apte, in Handbook of Integrated Risk Management for E-Business: Measuring, Modeling and Managing Risk, A. Labbi, eds., J.Ross Publishing, 2005.

2004

A Grid-based Approach for Enterprise-Scale Data Mining by R. Natarajan, R. Sion, C. Apte and I. Narang, in Proceedings of the Workshop on Data Mining and the Grid at the Fourth IEEE International Conference on Data Mining, November 2004.
Boosting as a Regularized Path to A Maximum Margin Classifier by S. Rosset, J. Zhu and T. Hastie. Journal of Machine Learning Research, 5(Aug):941-973. August 2004.
Tracking Curved Regularized Optimization Solution Paths by S. Rosset, in Neural Information Processing Systems, December 2004.
A Method for Inferring Label Sampling Mechanisms in Semi-Supervised Learning by S. Rosset, J. Zhu, H. Zou and T. Hastie, in Neural Information Processing Systems, December 2004.
The Entire Regularization Path for the Support Vector Machine by T. Hastie, S. Rosset, R. Tibshirani and J. Zhu. Journal of Machine Learning Research 5(Oct): 1391--1415, October 2004. R package (short version to appear in NIPS 2004).
An Iterative Method for Multi-Class Cost-Sensitive Learning by N. Abe, B. Zadrozny and J. Langord, in Proceedings of the International Conference on Knowledge Discovery and Data Mining, Seattle, August 2004.
Cross Channel Optimized Marketing by Reinforcement Learning by N. Abe, N. Verma, C. Apte and R. Schroko, in Proceedings of the International Conference on Knowledge Discovery and Data Mining, Seattle, August 2004.
Empirical Evaluation of Feature Subset Selection Based on a Real-world Data Set by P. Perner and C. Apte. Engineering Applications of Artificial Intelligence, Volume 17, Issue 3, Pages 285-288, April 2004.
Transform Regression and the Kolmogorov Superposition Theorem by E. Pednault. IBM Research Report RC-23227, June 2004.
Model Selection via the AUC by S. Rosset, in Proceedings of the Twenty-First International Conference on Machine Learning, July 2004.
Learning and Evaluating Classifiers under Sample Selection Bias by B. Zadrozny, in Proceedings of the Twenty-First International Conference on Machine Learning, July 2004.
Discussion of "Least Angle Regression" by Efron et al. by S. Rosset and Ji Zhu, in Annals of Statistics, April 2004 (preliminary version).
Sampling Approach to Resource Light Mining by N. Abe, C. Apte, B. Bhattacharjee, K. Goldman, J. Langford and B. Zadrozny, in Proceedings of the Data Mining in Resource Constrained Environments Workshop at the Fourth SIAM International Conference on Data Mining, April 2004.

2003

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.
Data Mining Analytics for Business Intelligence and Decision Support by C. Apte, in OR/MS Today, February 2003.
Data Intensive Analytics for Predictive Modeling by C. Apte, S.J. Hong, R. Natarajan, E.P.D. Pednault, F. Tipu, and S. Weiss, in IBM Journal of R&D, Vol. 47, No. 1, Pages 17-23, January 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.

Reinforcement Learning with Immediate Rewards and Linear Hypotheses by Naoki Abe, Alan Biermann, and Philip Long, in Algorithmica, 37, 263-293, 2003.

2002

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.

2001

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.

2000

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.

1999

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.

1998

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.

1997

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.
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.

1996 and before

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.
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 December 9, 2004