Optimization of Fuzzy Rules for Classification Using Genetic Algorithm (original) (raw)

Abstract

In this paper, we propose an efficient fuzzy rule generation algorithm based on fuzzy decision tree for high accuracy and better comprehensibility. We combine the comprehensibility of rules generated based on decision tree such as ID3 and C4.5 and the expressive power of fuzzy sets for dealing with quantitative data. Particularly, fuzzy rules allow us to effectively classify patterns of non-axis-parallel decision boundaries, which are difficult to do using attribute-based classification methods. We also investigate the use of genetic algorithm to optimize fuzzy decision trees in accuracy and comprehensibility by determining an appropriate set of membership functions for quantitative data. We have experimented our algorithm with several benchmark test data including manually generated two-class patterns, the iris data, the Wisconsin breast cancer data, and the credit screening data. The experiment results show that our method is more efficient in performance and comprehensibility of rules compared with methods including C4.5 and FID (Fuzzy ID3).

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Authors and Affiliations

  1. School of Computing, Soongsil University, 1-1, Sangdo 5-Dong, Dongjak-Gu, Seoul, Korea
    Myung Won Kim & Joung Woo Ryu
  2. Dept. of Computer Engineering, Hankyoung National University, 67, Seokjeong-dong, Ansung, Kyonggi-do, Korea
    Samkeun Kim
  3. FINANCE/SERVICE BUSSINESS UNIT, LG CNS Co., Ltd., Prime Tower 10-1, Hoehyun-Dong, 2-Ga, Jung-Gu, Seoul, Korea
    Joong Geun Lee

Authors

  1. Myung Won Kim
  2. Joung Woo Ryu
  3. Samkeun Kim
  4. Joong Geun Lee

Editor information

Editors and Affiliations

  1. Computer Science Department, Korea Advanced Institute of Science and Technology, 373-1 Koo-Sung Dong, Yoo-Sung Ku, Daejeon, 305-701, Korea
    Kyu-Young Whang
  2. Department of Statistics, Seoul National University, Sillimdong Kwanakgu, Seoul, 151-742, Korea
    Jongwoo Jeon
  3. School of Electrical Engineering and Computer Science, Seoul National University, Kwanak P.O. Box 34, Seoul, 151-742, Korea
    Kyuseok Shim
  4. Department of Computer Science and Engineering, University of Minnesota, 200 Union St SE, Minneapolis, MN, 55455, USA
    Jaideep Srivastava

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© 2003 Springer-Verlag Berlin Heidelberg

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Kim, M.W., Ryu, J.W., Kim, S., Lee, J.G. (2003). Optimization of Fuzzy Rules for Classification Using Genetic Algorithm. In: Whang, KY., Jeon, J., Shim, K., Srivastava, J. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2003. Lecture Notes in Computer Science(), vol 2637. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36175-8\_36

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