Title: A statistics based approach for extracting priority rules from trained neural networks
Abstract: In this paper, a statistics based approach named STARE (statistics-based rule extraction) that is designed to extract symbolic rules from trained neural networks is proposed. STARE deals with continuous attributes in a unique way so that not only different attributes could be discretized to different number of clusters but also unnecessary discretization could be avoided. STARE introduces statistics to the generation and evaluation of priority rules that have concise appearance. Since it is independent of the network architectures and training algorithms, STARE could be applied to diversified neural classifiers. Experimental results show that rules extracted via STARE are comprehensible, compact and accurate.
Publication Year: 2000
Publication Date: 2000-01-01
Language: en
Type: article
Indexed In: ['crossref']
Access and Citation
Cited By Count: 26
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