Browse > Article
http://dx.doi.org/10.5391/JKIIS.2003.13.2.209

A Construction of Fuzzy Model for Data Mining  

Kim, Do-Wan (Department of Electrical and Electronic Engineering, Yonsei University)
Joo, Young-Hoon (School of Electronic and Information Engineering, Kunsan National University)
Park, Jin-Bae (Department of Electrical and Electronic Engineering, Yonsei University)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.13, no.2, 2003 , pp. 209-215 More about this Journal
Abstract
A new GA-based methodology using information granules is suggested for the construction of fuzzy classifiers. The proposed scheme consists of three steps: selection of information granules, construction of the associated fuzzy sets, and tuning of the fuzzy rules. First, the genetic algorithm (GA) is applied to the development of the adequate information granules. The fuzzy sets are then constructed from the analysis of the developed information granules. An interpretable fuzzy classifier is designed by using the constructed fuzzy sets. Finally, the GA are utilized for tuning of the fuzzy rules, which can enhance the classification performance on the misclassified data (e.g., data with the strange pattern or on the boundaries of the classes). To show the effectiveness of the proposed method, an example, the classification of the Iris data, is provided.
Keywords
Fuzzy classifier; data mining; fuzzy set; information granules; genetic algorithm;
Citations & Related Records
연도 인용수 순위
  • Reference
1 T. P. Hong and C. Y. Lee, "Induction of fuzzy rules and membership functions from training examples," Fuzzy Sets Syst., vol. 84, no. 3, pp. 33-47, 1996.   DOI   ScienceOn
2 H. Roubos and M. Setnes "Compact transparent fuzzy models and classifiers through iterative complexity reduction," IEEE Trans. Fuzzy Systems, vol. 9, no. 4, pp. 516-524, 2001.   DOI   ScienceOn
3 R. A. Fisher, "The use of multiple measurements in taxonomic problems," Ann Eugenics., vol. 7, no. 2, pp. 179-188, 1936.   DOI
4 D. E. Goldberg, Genetic algorithms in searh, optimization, and machine learning. Addison-Wesley publishing company, Inc., 1989.
5 Y. H. Joo, H. S. Hwang, K. B. Kim, and K. B. Woo, "Fuzzy system modeling by fuzzy partition and GA hybrid schemes," Fuzzy Sets Syst., vol. 86, no. 3, pp. 279-288, 1997.   DOI   ScienceOn
6 S. M. Chen, M. S. Yeh, and P. Y. Hsiao, "A comparison of similarity measures of fuzzy values," Fuzzy Sets Syst., vol. 72, no. 1, pp. 79-89, 1995.   DOI   ScienceOn
7 L. A. Zadeh, "Fuzzy sets," Informat. Control, vol. 8, pp. 338-353, 1965.   DOI
8 Y. Shi, R. Eberhart, and Y. Chen, "Implementation of evolutionary fuzzy systems," IEEE Trans. Fuzzy Systems, vol. 7, no. 2, pp. 109-119, 1999.   DOI   ScienceOn
9 T. P. Wu and S. M. Chen, "A new method for constructing membership functions and fuzzy rules from training examples," IEEE Trans. Syst. Man, Cybern. B., vol. 29, no. 1, pp. 25-40, 1999.   DOI   ScienceOn
10 W. Pedrycz and A. Bargiela, "Granular clustering: a granular signature of data," IEEE Trans. Syst. Man, Cybern. B., vol. 32, no. 2, pp. 212-224, 2002.   DOI   ScienceOn
11 V. R. Young, "Fuzzy subsethood," Fuzzy Sets Syst., vol. 77, no. 3, pp. 371-384, 1996.   DOI   ScienceOn
12 H. Ishibuchi, K. Nozaki, N. Yamamoto, and H. Tanaka, "Selecting fuzzy if-then rules for classification problems using genetic algoritms," IEEE Trans. Fuzzy Systems, vol. 3, no. 3, pp. 260-270, 1995.   DOI   ScienceOn
13 S. Abe and M. S. Lan, "A method for fuzzy rules extraction directly from numerical data and its application to pattern classification," IEEE Trans. Fuzzy Systems, vol. 5, no. 1, pp. 358-368, 1995.
14 H. M. Lee, C. M. Chen, J. M. Chen, and Y. L. Jou, "An efficient fuzzy classifier with feature selection based on fuzzy entropy," IEEE Trans. Syst. Man, Cybern. B., vol. 3, no. 3, pp. 426-432, 1997.
15 S. Abe and R. Thawonmas, "A fuzzy classifier with ellipsoidal regions," IEEE Trans. Fuzzy Systems, vol. 5, no. 3, pp. 358-368, 1997.   DOI   ScienceOn
16 Y. H. Joo, H. S. Hwang, K B. Kim, and K. B. Woo, "Linguistic model identification for fuzzy system," Electron Letter, vol. 31, no. 4, pp. 330-331, 1995.   DOI   ScienceOn
17 L. X. Wang and J. M. Mendel, "Generating fuzzy rules by learning from examples," IEEE Trans. Syst. Man, Cybern B., vol. 22, no. 6, pp. 1414-1427, 1992.   DOI   ScienceOn
18 S. Halgamuge and M. Glesner, "Neural networks in designing fuzzy systems for real world applications," Fuzzy Sets Syst., vol. 65, pp. 1-12, 1994.   DOI   ScienceOn
19 R. Thawonmas and S. Abe, "A novel approach to feature selection based on analysis of class regions," IEEE Trans. Syst. Man. Cybern B., vol. 27, no. 2, pp. 196-207, 1997.   DOI   ScienceOn