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Learning with globally predictive tests

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Abstract

We introduce a new bias for rule learning systems. The bias only allows a rule learner to create a rule that predicts class membership if each test of the rule in isolation is predictive of that class. Although the primary motivation for the bias is to improve the understandability of rules, we show that it also improves the accuracy of learned models on a number of problems. We also introduce a related preference bias that allows creating rules that violate this restriction if they are statistically significantly better than alternative rules without such violations.

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Author information

Authors and Affiliations

  1. Department of Information and Computer Science, University of California, 92697, Irvine, California, USA

    Michael J. Pazzani

Authors
  1. Michael J. Pazzani
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Additional information

Michael J. Pazzani, Ph.D.: He is a Full Professor and Chair in the Department of Information and Computer Science at the University of California, Irvine. He obtained his bachelors degree from the University of Connecticut in 1980 and his Ph. D. from University of California, Los Angles in 1987. His research interests are in machine learning, cognitive modeling and information access. He has published over 100 research papers and 2 books. He has served on the Editorial Board of the Machine Learning and the Journal of Artificial Intelligence Research.

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Pazzani, M.J. Learning with globally predictive tests. New Gener Comput 18, 29–38 (2000). https://doi.org/10.1007/BF03037566

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  • DOI: https://doi.org/10.1007/BF03037566

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