Abstract
In this paper, we examine the effect of weighting training patterns on the performance of fuzzy rule-based classification systems. A weight is assigned to each given pattern based on the class distribution of its neighboring given patterns. The values of weights are determined proportionally by the number of neighboring patterns from the same class. Large values are assigned to given patterns with many patterns from the same class. Patterns with small weights are not considered in the generation of fuzzy rule-based classification systems. That is, fuzzy if-then rules are generated from only patterns with large weights. These procedures can be viewed as preprocessing in pattern classification. The effect of weighting is examined for an artificial data set and several real-world data sets.
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References
Sugeno, M.: An Introductory Survey of Fuzzy Control. Information Science 30(1/2), 59–83 (1985)
Lee, C.C.: Fuzzy Logic in Control Systems: Fuzzy Logic Controller Part I and Part II. IEEE Trans. Syst., Man, Cybern. 20, 404–435 (1990)
Leondes, C.T. (ed.): Fuzzy theory Systems: Techniques and Applications, vol. 1-4. Academic Press, San Diego (1999)
Ishibuchi, H., Nakashima, T.: Performance evaluation of fuzzy classifier systems for multi-dimensional pattern classification problems. IEEE Trans. on Syst., Man, Cybern. Part B 29, 601–618 (1999)
Ishibuchi, H., Nakashima, T.: Improving the performance of fuzzy classifier systems for pattern classification problems with continuous attributes. IEEE Trans. on Industrial Electronics 46(6), 1057–1068 (1999)
Yuan, Y., Zhang, H.: A genetic algorithms for generating fuzzy classification rules. Fuzzy Sets and Systems, Vol 84(1), 1–19 (1996)
Klir, G.J., Yuan, B.: Fuzzy Sets and Fuzzy Logic. Prentice-Hall, Englewood Cliffs (1995)
Ishibuchi, H., Nozaki, K., Tanaka, H.: Distributed representation of fuzzy rules and its application to pattern classification. Fuzzy Sets and Systems 52(1), 21–32 (1992)
Ishibuchi, H., Nozaki, K., Yamamoto, N., Tanaka, H.: Selecting fuzzy if-then rules for classification problems using genetic algorithms. IEEE Trans. on Fuzzy Systems 3(3), 260–270 (1995)
Ishibuchi, H., Nakashima, T.: Effect of rule weights in fuzzy rule-based classification systems. IEEE Trans. on Fuzzy Systems. 9(4), 506–515 (2001)
Nozaki, K., Ishibuchi, H., Tanaka, H.: Adaptive fuzzy rule-based classification systems. IEEE Trans. on Fuzzy Systems 4(3), 238–250 (1996)
Fisher, R.A.: The use of multiple measurements in taxonomic problems. Ann. of Eugenics 7, 179–188 (1936)
Weiss, S.M., Kulikowski, C.A.: Computer systems that learn. Morgan Kaufmann, San Mateo (1991)
Grabisch, M., Dispot, F.: A comparison of some methods of fuzzy classification on real data. In: Proc. of 2nd Intl. Conf. on Fuzzy Logic and Neural Networks, pp. 659–662 (1992)
Grabisch, M.: The representation of importance and interaction of features by fuzzy measures. Pattern Recognition Lett. 17, 567–575 (1996)
Grabisch, M., Nicolas, J.-M.: Classification by fuzzy integral: performance and tests. Fuzzy Sets and Systems 65(2/3), 255–271 (1994)
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© 2004 Springer-Verlag Berlin Heidelberg
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Nakashima, T., Ishibuchi, H., Bargiela, A. (2004). A Study on Weighting Training Patterns for Fuzzy Rule-Based Classification Systems. In: Torra, V., Narukawa, Y. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2004. Lecture Notes in Computer Science(), vol 3131. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-27774-3_7
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DOI: https://doi.org/10.1007/978-3-540-27774-3_7
Publisher Name: Springer, Berlin, Heidelberg
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