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A Efficient Rule Extraction Method Using Hidden Unit Clarification in Trained Neural Network  

Lee, Hurn-joo (고려대학교 정보대학 컴퓨터학과)
Kim, Hyeoncheol (고려대학교 정보대학 컴퓨터학과)
Publication Information
The Journal of Korean Association of Computer Education / v.21, no.1, 2018 , pp. 51-58 More about this Journal
Recently artificial neural networks have shown excellent performance in various fields. However, there is a problem that it is difficult for a person to understand what is the knowledge that artificial neural network trained. One of the methods to solve these problems is an algorithm for extracting rules from trained neural network. In this paper, we extracted rules from artificial neural networks using ordered-attribute search(OAS) algorithm, which is one of the methods of extracting rules, and analyzed result to improve extracted rules. As a result, we have found that the distribution of output values of the hidden layer unit affects the accuracy of rules extracted by using OAS algorithm, and it is suggested that efficient rules can be extracted by binarizing hidden layer output values using hidden unit clarification.
Artificial Neural Network; Rule Extraction; Hidden Unit Clarification;
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