Yung-Chou Chea, Li-Hui Wangb, and Shyi-Ming Chenc*

a Department of Electronic Engineering, National Taiwan University of Science and Technology, Taipei 106, Taiwan, R.O.C.
b Department of Finance, Chihlee Institute of Technology, Banciao City, Taipei County 220, Taiwan, R.O.C.
c Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei 106, Taiwan, R.O.C.


 

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ABSTRACT


The most important task in the design of fuzzy classification systems is to find a set of fuzzy rules from training data to deal with a specific classification problem. In this paper, we present a new method to generate weighted fuzzy rules from training data to deal with the Iris data classification problem. First, we convert the training data to fuzzy rules, and then we merge those fuzzy rules in order to reduce the number of fuzzy rules. Then, we calculate the weight of each input variable appearing in the generated fuzzy rules by the relationships of input variables. The proposed weighted fuzzy rules generation method gets a higher average classification accuracy rate than the existing methods.


Keywords: fuzzy classification systems; fuzzy sets; Iris data; membership functions; weighted fuzzy rules.


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REFERENCES


[1] Castro, J. L., Castro-Schez, J. J., and Zurita, J. M. 1999. Learning maximal structure rules in fuzzy logic for knowledge acquisition in expert systems. Fuzzy Sets and Systems, 101, 3: 331-342.

[2] Castro, J. L. and Zurita, J. M. 1997. An inductive learning algorithm in fuzzy systems. Fuzzy Sets and Systems, 89, 2: 193-203.

[3] Chang, C. H. and Chen, S. M. 2000. A new method to generate fuzzy rules from numerical data based on the exclusion of attribute terms. Proceedings of the 2000 International Computer Symposium: Workshop on Artificial Intelligence, Chiayi, Taiwan, Republic of China: 57-64.

[4] Chen, S. M., Lee, S. H., and Lee, C. H. 1999. A new method for generating fuzzy rules from numerical data for handling fuzzy classification problems. Applied Artificial Intelligence: An International Journal, 33, 8: 645-664.

[5] Chen, S. M. and Yeh, M. S. 1998. Generating fuzzy rules from relational database systems for estimating null values. Cybernetics and Systems: An International Journal, 29, 6: 363-376.

[6] Chen, M. and Lin, S. Y. 2000. A new method for constructing fuzzy decision trees and generating fuzzy classification rules from training examples. Cybernetics and Systems: An International Journal, 31, 7: 763-785.

[7] Chen, S. M., Kao, C. H., and Yu, C. H. 2002. Generating fuzzy rules from training data containing noise for handling classification problems. Cybernetics and Systems: An International Journal, 33, 7: 723-749.

[8] Chen, S. M. and Chen, Y. C. 2002. Automatically constructing membership functions and generating fuzzy rules using genetic algorithms. Cybernetics and Systems: An International Journal, 33, 8: 841-863.

[9] Chen, S. M. and Yu, C. H. 2004. A new method for handling fuzzy classification problems using clustering techniques. International Journal of Applied Science and Engineering, 2, 1: 90-104.

[10] Chen, C. and Chen, S. M. 2000. A new method to generate fuzzy rules for fuzzy classification systems. Proceedings of the 2000 Eighth National Conference on Fuzzy Theory and Its Applications, Taipei, Taiwan, Republic of China.

[11] Fisher, R. 1936. The use of multiple measurements in taxonomic problems. Eugenics, 7: 179-188.

[12] Hayushi, Y. 1992. Fuzzy neural expert system with automated extraction of fuzzy if-then rules from a trained neural network. In “Analysis and Management of Uncertainty: Theory and Applications” (edited by Ayyub, B. M., Gupta, M. M., and Kanal, L. N.), North-Holland, Amsterdam: 171-181.

[13] Ishibuchi, H. and Tanaka, H. 1993. Neural network that learn from fuzzy If-Then rules. IEEE Transactions on Fuzzy Systems, 1, 1: 85-97.

[14] Kasabov, N. K. 1996. Learning fuzzy rules and approximate reasoning in fuzzy neural networks and hybrid systems. Fuzzy Sets and Systems, 82, 2: 135-149.

[15] Lin, H. L. and Chen, S. M. 2000. Generating weighted fuzzy rules from training data for handling fuzzy classification problems. Proceedings of the 2000 International Computer Symposium: Workshop on Artificial Intelligence, Chiayi, Taiwan, Republic of China: 11-18.

[16] Nozaki, K., Ishihuchi, H., and Tanaka, H. 1997. A simple but powerful heuristic method for generating fuzzy rules from numerical sets. Fuzzy Sets and Systems, 86, 3: 251-270.

[17] Wang, L. X. and Mendel, J. M. 1992. Generating fuzzy rules by learning from examples. IEEE Transactions on Systems, Man, and Cybernetics, 22, 6: 1414-1427.

[18] Wu, T. P. and Chen, S. M. 1999. A new method for constructing membership functions and fuzzy rules from training examples. IEEE Transactions on systems, Man, and Cybernetics-Part B: Cybernetics, 29, 1: 25-40.

[19] Yao, J. M., Dash, M., Tan, S. T., and Liu, H. 2000. Entropy-based fuzzy clustering and fuzzy modeling. Fuzzy Sets and Systems, 113, 3: 381-388.

[20] Yuan, Y. and Shaw, M. J. 1995. Induction of fuzzy decision trees. Fuzzy Sets and Systems, 69, 2: 125-139.

[21] Yuan, Y. and Zhuang, H. 1996. A genetic algorithm for generating fuzzy classification rules. Fuzzy Sets and Systems, 84, 1: 1-19.

[22] Zadeh, L. A. 1965. Fuzzy sets. Information and Control, 8: 338-353.


ARTICLE INFORMATION




Accepted: 2005-08-17
Available Online: 2006-04-04


Cite this article:

Chen, Y.-C., Wang, L.-H., Chen, S.-M. 2006. Generating weighted fuzzy rules from training data for dealing with the iris data classification problem. International Journal of Applied Science and Engineering, 4, 41–52. https://doi.org/10.6703/IJASE.2006.4(1).41