Classification Learning Using All Rules
Lecture Notes in Computer Science 1398: Proceedings of the Tenth European Conference on Machine Learning (ECML'98), page 149-159. Berlin/Heidelberg, Springer, (1998)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 explains 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 form of this nomination of redundant classifiers. This paper presents an algorithm that in effect learns all classifiers. Preliminary investigations by Webb (1996b) suggest 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.
