Incremental Rule Pruning for Fuzzy ARTMAP Neural Network

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Abstract

Fuzzy ARTMAP is capable of incrementally learning interpretable rules. To remove unused or inaccurate rules, a rule pruning method has been proposed in the literature. This paper addresses its limitations when incremental learning is used, and modifies it so that it does not need to store previously learnt samples. Experiments show a better performance, especially in concept drift problems.