進化型多目的最適化手法を用いたファジィルール選択 Fuzzy Rule Selection Using Evolutionary Multiobjective Optimization Methods

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One advantage of evolutionary multiobjective optimization (EMO) algorithms over classical approaches is that many non-dominated solutions can be simultaneously obtained by their single run. This paper shows how this advantage can be utilized in genetic rule selection for the design of fuzzy rule-based classification systems. Our genetic rule selection is a two-stage approach. In the first stage, a pre-specified number of candidate rules are extracted from numerical data using a data mining technique. In the second stage, an EMO algorithm is used for finding non-dominated rule sets with respect to three objectives. Since one of the three objectives is to maximize a classification rate on training patterns, the evolution of rule sets tends to overfit to training patterns. The question is whether the other two objectives with respect to complexity work as a safeguard against the over-fitting. In this paper, we examine the effect of the three-objective formulation on the generalization ability of obtained rule sets through computational experiments where many non-dominated rule sets are generated using an EMO algorithm for a number of high-dimensional pattern classification problems. Finally, we demonstrate that an ensemble of generated fuzzy rule-based systems leads to high generalization ability.

収録刊行物

  • システム制御情報学会論文誌

    システム制御情報学会論文誌 17(7), 278-287, 2004-07-15

    一般社団法人 システム制御情報学会

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各種コード

  • NII論文ID(NAID)
    10013226321
  • NII書誌ID(NCID)
    AN1013280X
  • 本文言語コード
    JPN
  • 資料種別
    ART
  • ISSN
    13425668
  • NDL 記事登録ID
    7002657
  • NDL 雑誌分類
    ZM11(科学技術--科学技術一般--制御工学)
  • NDL 請求記号
    Z14-195
  • データ提供元
    CJP書誌  NDL  J-STAGE 
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