A Quantitative Study of Small Disjuncts

Gary M. Weiss and Haym Hirsh, Rutgers University

Systems that learn from examples often express the learned concept in the form of a disjunctive description. Disjuncts that correctly classify few training examples are known as small disjuncts and are interesting to machine learning researchers because they have a much higher error rate than large disjuncts. Previous research has investigated this phenomenon by performing ad hoc analyses of a small number of datasets. In this paper we present a quantitative measure for evaluating the effect of small disjuncts on learning, and use it to analyze thirty benchmark datasets. We investigate the relationship between small disjuncts and pruning, training set size and noise to an extent that was not previously possible.

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