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Rule Extraction: From Neural Architecture To Symbolic Representation, Gail A. Carpenter, Ah-Hwee Tan
Rule Extraction: From Neural Architecture To Symbolic Representation, Gail A. Carpenter, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
This paper shows how knowledge, in the form of fuzzy rules, can be derived from a supervised learning neural network called fuzzy ARTMAP. Rule extraction proceeds in two stages: pruning, which simplifies the network structure by removing excessive recognition categories and weights; and quantization of continuous learned weights, which allows the final system state to be translated into a usable set of descriptive rules. Three benchmark studies illustrate the rule extraction methods: (1) Pima Indian diabetes diagnosis, (2) mushroom classification and (3) DNA promoter recognition. Fuzzy ARTMAP and ART-EMAP are compared with the ADAP algorithm, the k nearest neighbor system, …