Designing a decompositional rule extraction algorithm for neural networks with bound decomposition tree

Jia Sheng Heh, Jen Cheng Chen, Maiga Chang

    Research output: Contribution to journalJournal Articlepeer-review

    5 Citations (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
    Pages (from-to)297-309
    Number of pages13
    JournalNeural Computing and Applications
    Volume17
    Issue number3
    DOIs
    Publication statusPublished - Jun. 2008

    Keywords

    • Boolean rule
    • Neural network
    • Rule extraction

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