Rule Induction and Instance-Based Learning: A Unified Approach

This paper presents a new approach to inductive learning that combines aspects of instancebased learning and rule induction in a single simple algorithm. The RISE system searches for rules in a speci c-to-general fashion, starting with one rule per training example, and avoids some of the di culties of separate-andconquer approaches by evaluating each proposed induction step globally, i.e., through an e cient procedure that is equivalent to checking the accuracy of the rule set as a whole on every training example. Classi cation is performed using a best-match strategy, and reduces to nearest-neighbor if all generalizations of instances were rejected. An extensive empirical study shows that RISE consistently achieves higher accuracies than state-of-the-art representatives of its \parent" paradigms (PEBLS and CN2), and also outperforms a decision-tree learner (C4.5) in 13 out of 15 test domains (in 10 with 95% con dence).