Generalization of Classification Rules

Abstract

Traditional classification rules are in the form of production rules. Recent works in hybrid classification algorithms have proposed the generation of contextual rules, whereby the right-hand side of the production rule is replaced by a classifier, to achieve higher accuracy. In this work, we present a framework to further generalize classification rules such that the left-hand side of a production rule is expressed as a conjunction of classifiers, called space splitters. An intelligent divide-and-conquer approach is designed to construct such generalized classification rules. The construction algorithm, GCTree, is elegant, efficient and scalable. The resulting classifier is able to achieve high predictive accuracy that outperforms Naïve Bayes and C4.5. Experiments demonstrate that GCTree is compact and stable.