A Statistics Based Approach for Extracting Priority Rules from Trained Neural Networks

IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 3 p. 3401
A Statistics Based Approach for Extracting Priority Rules from Trained Neural Networks
Zhi-Hua Zhou, Nanjing University
Shi-Fu Chen, Nanjing University
Zhao-Qian Chen, Nanjing University

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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/IJCNN.2000.861337
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Abstract
In this paper, a statistics based approach named STARE 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.
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Citation: Zhi-Hua Zhou, Shi-Fu Chen, Zhao-Qian Chen, "A Statistics Based Approach for Extracting Priority Rules from Trained Neural Networks," ijcnn, p. 3401, IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 3, 2000

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