An information theoretic approach to rule induction from databases

An algorithm for the induction of rules from examples is introduced. The algorithm is novel in the sense that it not only learns rules for a given concept (classification), but it simultaneously learns rules relating multiple concepts. This type of learning, known as generalized rule induction, is considerably more general than existing algorithms, which tend to be classification oriented. Initially, it is focused on the problem of determining a quantitative, well-defined rule preference measure. In particular, a quantity called the J-measure is proposed as an information-theoretic alternative to existing approaches. The J-measure quantifies the information content of a rule or a hypothesis. The information theoretic origins of this measure are outlined, and its plausibility as a hypothesis preference measure is examined. The ITRULE algorithm, which uses the measure to learn a set of optimal rules from a set of data samples, is defined. Experimental results on real-world data are analyzed