On learning multiple descriptions of a concept

In sparse data environments, greater classification accuracy can be achieved by learning several concept descriptions of the data and combining their classifications. Stochastic searching can be used to generate many concept descriptions (rule sets) for each class in the data. We use a tractable approximation to the optimal Bayesian method for combining classifications from such descriptions. The primary result of this paper is that multiple concept descriptions are particularly helpful in “flat” hypothesis spaces in which there are many equally good ways to grow a rule, each having similar gain. Another result is experimental evidence that learning multiple rule sets yields more accurate classifications than learning multiple rules for some domains