Classification learning using all rules

Abstract

The covering algorithm has been ubiquitous in the induction of classification rules. This approach to machine learning uses heuristic search that seeks to find a minimum number of rules that adequately explain the data. However, recent research has provided evidence that learning redundant classifiers can increase predictive accuracy. Learning all possible classifiers seems to be a plausible ultimate form of this notion of redundant classifiers. This paper presents an algorithm that in effect learns all classifiers. Preliminary investigation by Webb (1996b) suggested that a heuristic covering algorithm in general learns classification rules with higher predictive accuracy than those learned by this new approach. In this paper we present an extensive empirical comparison between the learning-all-rules algorithm and three varied established approaches to inductive learning, namely, a covering algorithm, an instance-based learner and a decision tree learner. Empirical evaluation provides strong evidence in support of learning-all-rules as a plausible approach to inductive learning.

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