Induction of recursive bayesian classifiers

Abstract

In this paper, we review the induction of simple Bayesian classifiers, note some of their drawbacks, and describe a recursive algorithm that constructs a hierarchy of probabilistic concept descriptions. We posit that this approach should outperform the simpler scheme in domains that involve disjunctive concepts, since they violate the independence assumption on which the latter relies. To test this hypothesis, we report experimental studies with both natural and artificial domains. The results are mixed, but they are encouraging enough to recommend closer examination of recursive Bayesian classifiers in future work.