An interpretable neural network ensemble

Pitoyo Hartono*, Shuji Hashimoto


    研究成果: Conference contribution

    3 被引用数 (Scopus)


    The objective of this study is to build a model of neural network classifier that is not only reliable but also, as opposed to most of the presently available neural networks, logically interpretable in a human-plausible manner. Presently, most of the studies of rule extraction from trained neural networks focus on extracting rule from existing neural network models that were designed without the consideration of rule extraction, hence after the training process they are meant to be used as a kind black box. Consequently, this makes rule extraction a hard task. In this study we construct a model of neural network ensemble with the consideration of rule extraction. The function of the ensemble can be easily interpreted to generate logical rules that are understandable for human. We believe that the interpretability of neural networks contributes to the improvement of the reliability and the usability of neural networks when applied to critical real world problems.

    ホスト出版物のタイトルIECON Proceedings (Industrial Electronics Conference)
    出版ステータスPublished - 2007
    イベント33rd Annual Conference of the IEEE Industrial Electronics Society, IECON - Taipei
    継続期間: 2007 11月 52007 11月 8


    Other33rd Annual Conference of the IEEE Industrial Electronics Society, IECON

    ASJC Scopus subject areas

    • 電子工学および電気工学


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