Fuzzy Rule Selection Using Evolutionary Multiobjective Optimization Methods
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- YAMAMOTO Takashi
- Department of Industrial Engineering, Osaka Prefecture University
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- ISHIBUCHI Hisao
- Department of Industrial Engineering, Osaka Prefecture University
Bibliographic Information
- Other Title
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- 進化型多目的最適化手法を用いたファジィルール選択
- シンカガタ タモクテキ サイテキカ シュホウ オ モチイタ ファジィルール センタク
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Abstract
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.
Journal
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- Transactions of the Institute of Systems, Control and Information Engineers
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Transactions of the Institute of Systems, Control and Information Engineers 17 (7), 278-287, 2004
THE INSTITUTE OF SYSTEMS, CONTROL AND INFORMATION ENGINEERS (ISCIE)
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Details
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- CRID
- 1390001205165869184
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- NII Article ID
- 10013226321
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- NII Book ID
- AN1013280X
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- ISSN
- 2185811X
- 13425668
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- NDL BIB ID
- 7002657
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- Text Lang
- ja
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- Data Source
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- JaLC
- NDL
- Crossref
- CiNii Articles
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- Abstract License Flag
- Disallowed