Stephen Bay

Institute for the Study of Learning and Expertise 2164 Staunton Court Palo Alto, CA 94306 USA Tel: +1(650) 723-1684 Email: sbay@apres.stanford.edu I am a research scientist at the Institute for the Study of Learning and Expertise and a member of the Computational Learning Laboratory in the Center for the Study of Language and Information at Stanford University.

Research

• Detecting outliers in large data sets.
• Revising models of gene regulation.
• Monitoring and Modeling the Space Station Electrical Power System.
• Detecting group differences. An important descriptive analysis task is understanding the differences between several contrasting groups from observational data. These groups could represent different categories of objects, such as male or female students, or the same category over multiple time periods as with consecutive cohorts of students (e.g. computer science students in 1993 through to 1999).
• Cognitive aspects of knowledge discovery. To date most work in data mining has not paid attention to cognitive factors which affect the usability of discovered results by humans. Thus many mining algorithms return results that either not credible or are just not interesting to a user. I have begun investigating some cognitive factors which affect humans ability to use data mining results.
• Combining nearest neighbor classifiers. There are many combining algorithms that dramatically improve the performance of classifiers such as decision trees and neural networks but are not effective with nearest neighbor classifiers. I examined methods for combining nearest neighbor classifiers to improve accuracy.

Knowledge Discovery in Databases Archive

Publications

Chrisman, L., Langley, P., Bay, S. D., and Pohorille, A. (2003). Incorporating biological knowledge into evaluation of causal regulatory hypotheses. Proceedings of the Pacific Symposium on Biocomputing. Postscript.

Saito, K., Bay, S. D., and Langley, P. (2002). Revising Qualitative Models of Gene Regulation. Proceedings of the Fifth International Conference on Discovery Science. Lubeck, Germany. Postscript. PDF.

Bay, S. D., Shapiro, D. G., and Langley, P. (2002). Revising engineering models: Combining computational discovery with knowledge. Proceedings of the Thirteenth European Conference on Machine Learning. Helsinki, Finland. Postscript. PDF.

Bay, S. D. (2001). Clustering and Merging Geographic Regions Based on Sample Information. PAKDD workshop on Spatial and Temporal Data Mining. Hong Kong.

Bay, S. D. and Pazzani, M. J. (2001). Detecting Group Differences: Mining Contrast Sets. Data Mining and Knowledge Discovery. Abstract. Postscript. PDF.

Bay, S. D. (2001). Multivariate Discretization for Set Mining. Knowledge and Information Systems. Abstract. Postscript. PDF.

Bay, S. D., Kibler, D., Pazzani, M. J., and Smyth, P. (2001). The UCI KDD Archive of Large Data Sets for Data Mining Research and Experimentation. Information Processing Society of Japan Magazine. Volume 42, Number 5, pages 462-466. English language version reprinted in SIGKDD Explorations. Volume 2, Issue 2, pages 81-85, 2000. Abstract. Postscript. PDF.

Bay, S. D. (2000). Multivariate Discretization of Continuous Variables for Set Mining. Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Abstract. Postscript. PDF.

Bay, S. D. and Pazzani, M. J. (2000). Discovering and Describing Category Differences: What makes a discovered difference insightful?. Proceedings of the Twenty Second Annual Meeting of the Cognitive Science Society. Abstract. Postscript. PDF.

Bay, S. D. and Pazzani, M. J. (2000). Characterizing Model Errors and Differences. Proceedings of the Seventeenth International Conference on Machine Learning. Abstract. Postscript. PDF. Slides.

Bay, S. D. and Pazzani, M. J. (2000). Characterizing Model Performance in the Feature Space. ICML 2000 Workshop on What Works Well Where?. Postscript. PDF. handout instructions. handout 1. handout 2. handout 3.

Bay, S. D., Kibler, D., Pazzani, M. J., and Smyth, P. (2000). The UC Irvine Knowledge Discovery in Databases Archive. The 32nd Symposium on the Interface: Computing Science and Statistics. Invited poster.

Bay, S. D. and Pazzani, M. J. (1999). Detecting Change in Categorical Data: Mining Contrast Sets. Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Abstract. Postscript. PDF.

Pazzani, M. J. and Bay, S. D. (1999). The Independent Sign Bias: Gaining Insight from Multiple Linear Regression. Proceedings of the Twenty-First Annual Meeting of the Cognitive Science Society. Abstract. PDF. Postscript. Slides.

Bay, S. D. (1999). Nearest Neighbor Classification from Multiple Feature Subsets. Intelligent Data Analysis. 3(3):191-209. Abstract. Postscript. PDF. (preprint).

Bay, S. D. (1998). Combining Nearest Neighbor Classifiers Through Multiple Feature Subsets. Proceedings of the Fifteenth International Conference on Machine Learning. Madison, WI. Abstract. Postscript. PDF.

Bay, S. D. (1997). Nearest Neighbour Classification from Multiple Data Representations. Master's thesis, University of Waterloo, Department of Systems Design Engineering.

My BibTeX file myrefs.bib.

Software

MFS. Nearest Neighbor Ensembles based on Multiple Feature Subsets.

Teaching

I taught ICS 171: Introduction to Artificial Intelligence at UCI in Summer 2000. The course webpage has detailed problem sets with solutions. If you are an instructor, I can make available the latex source.

Fun Stuff