The RISE 2.0 System: A Case Study in Multistrategy Learning

Several well-developed approaches to inductive learning now exist, but each has speci c limitations that are hard to overcome. Multi-strategy learning attempts to tackle this problem by combining multiple methods in one algorithm. This report describes a uni cation of two widely-used empirical approaches: rule induction and instance-based learning. In the new algorithm, instances are treated as maximally speci c rules, and classi cation is performed using a best-match strategy. Rules are learned by gradually generalizing instances until no improvement in apparent accuracy is obtained. Theoretical analysis shows this approach to be e cient. It is implemented in the RISE 2.0 system. In an extensive empirical study, RISE consistently outperforms state-of-the-art representatives of both its parent approaches (PEBLS and CN2), as well as a decision tree learner (C4.5). Most signi cantly, in 15 of the domains studied, RISE achieves higher accuracy than the best of PEBLS and CN2, showing that a signi cant synergy can be obtained by combining multiple empirical methods.