The Problem with Noise and Small Disjuncts

Many systems that learn from examples express the learned concept as a disjunction. Those disjuncts that cover only a few examples are referred to as small disjuncts. The problem with small disjuncts is that they have a much higher error rate than large disjuncts but are necessary to achieve a high level of predictive accuracy. This paper investigates the effect of noise on small disjuncts. In particular, we show that when noise is added to two real-world domains, a significant, and disproportionate number of the total errors are contributed by the small disjuncts; thus, in the presence of noise, it is the small disjuncts that are primarily responsible for the poor predictive accuracy of the learned concept.

• Type: Conference
• Year: 1998
• Where: ICML
• Authors: Gary M. Weiss, Haym Hirsh
• Keywords: ICML 1998, Machine Learning, Many Systems, Predictive Accuracy, Small Disjuncts