Designing a decompositional rule extraction algorithm for neural networks

Jen Cheng Chen, Jia Sheng Heh, Maiga Chang

    Research output: Chapter in Book/Report/Conference proceedingPublished Conference contributionpeer-review

    1 Citation (Scopus)

    Abstract

    The neural networks are successfully applied to many applications in different domains. However, due to the results made by the neural networks are difficult to explain the decision process of neural networks is supposed as a black box. The explanation of reasoning is important to some applications such like credit approval application and medical diagnosing software. Therefore, the rule extraction algorithm is becoming more and more important in explaining the extracted rules from the neural networks. In this paper, a decompositional algorithm is analyzed and designed to extract rules from neural networks. The algorithm is simple but efficient; can reduce the extracted rules but improve the efficiency of the algorithm at the same time. Moreover, the algorithm is compared to the other two algorithms, M-of-N and Garcez, by solving the MONK's problem.

    Original languageEnglish
    Title of host publicationAdvances in Neural Networks - ISNN 2006
    Subtitle of host publicationThird International Symposium on Neural Networks, ISNN 2006, Proceedings
    Pages1305-1311
    Number of pages7
    DOIs
    Publication statusPublished - 2006
    Event3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks - Chengdu, China
    Duration: 28 May 20061 Jun. 2006

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume3971 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    Conference3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks
    Country/TerritoryChina
    CityChengdu
    Period28/05/061/06/06

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