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Open Access Publications from the University of California

The RISE 2.0 system : a case study in multistrategy learning


Several well-developed approaches to inductive learning now exist, but each has specific 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 unification of two widely-used empirical approaches: rule induction and instance-based learning. In the new algorithm, instances are treated as maximally specific rules, and classification 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 efficient. 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 significantly, in 15 of the domains studied, RISE achieves higher accuracy than the best of PEBLS and CN2, showing that a significant synergy can be obtained by combining multiple empirical methods.

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