Rule Extraction: From Neural Representation Architecture to Symbolic
نویسندگان
چکیده
This paper shows how knowledge, in the form of fuzzy rules, can be derizted from a superuised learning neural network called fuzzy ARTMAP. Rule extraction proceeds in two stages: pruning, which simplifies the network structure by remooing excessiae recognition categories and weights; and quantization of continuous learned weights, which allows the final system state to be translated into a usable set of descriptizte rules. Three benchmark studies illustrate the rule extraction methods: (l) 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, the back-propqgation network and the C45 decision tree. The ARTMAP rule extraction procedure is also compared with the Knowledgetron and lr{opM algorithms, which extract rules from back-propagation networks. Simulation results consistently indicate that ARTMAP rule extraction produces compact sets of comprehensible rules for which accuracy and complexity compare faztorably to rules extracted by ahernatizte algorithms.
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