Pessimistic and Optimistic Induction

Learning methods vary in the optimism or pessimism with which they regard the informativeness of learned knowledge. Pessimism is implicit in hypothesis testing, where we wish to draw cautious conclusions from experimental evidence. However, this paper demonstrates that optimism in the utility of derived rules may be the preferred bias for learning systems themselves. We examine the continuum between naive pessimism and naive optimism in the context of a decision tree learner that prunes rules based on stringent (i.e., pessimistic) or weak (i.e., optimistic) tests of their signiicance. Our experimental results indicate that in most cases optimism is preferred, but particularly in cases of sparse training data and high noise. This work generalizes earlier ndings by Fisher and Schlimmer (1988) and Schaaer (1992), and we discuss its relevance to unsupervised learning, small disjuncts, and other issues.