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Abstrakty
Continuous attributes are usually discretized into intervals in machine learning and data mining. Our knowledge representation is, however, not always based on such discretization. For example, we usually use linguistic terms for dividing our ages into some categories with fuzzy boundaries. In this paper, we examine the effect of fuzzy discretization on the classification performance of fuzzy rule-based systems through computer simulations on simple numerical examples and real-world pattern classification problems. For exulting such computer simulations, we introduce a control parameter that specifies the overlap grade between adjacent antecedent fuzzy sets in fuzzy discretization. Interval discretization can be viewed as a special case of fuzzy discretization with no overlap. Computer simulations are performed using fuzzy discretization with various specifications of the overlap grade. Simulation results show that fuzzy rules have high generalization ability even when the domain interval of each continuous attribute is homogeneously partitioned into linguistic terms. On the other hand, generalization ability of rule-based systems strongly depends on the choice of theshold values in the case of interval discretization.
Czasopismo
Rocznik
Tom
Strony
351--377
Opis fizyczny
Bibliogr. 15 poz., rys.
Twórcy
autor
- Department of Industrial Engineering, Osaka Prefecture University, Sakai 599-8531 Japan
autor
Bibliografia
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- [7] H. Ishibuchi, T. Nakashima and T. Morisawa: Voting in fuzzy rule-based systems for pattern classification problems. Fuzzy Sets and Systems. 103(2), (1999), 223-238.
- [8] H. Ishibuchi, T. Nakashima and T. Murata: Three-objective genetics-based machine learning for linguistic rule extraction. Information Sciences. 136(1-4), (2001), 109-133.
- [9] H. Ishibuchi, K. Nozaki and H. Tanaka: Distributed representation of fuzzy rules and its application to pattern classification. Fuzzy Sets and Systems. 52(1), (1992), 21-32.
- [10] H. Ishibuchi, T. Yamamoto and T. Nakashima: Fuzzy data mining: Effect of fuzzy discretization. Proc. 1st IEEE Int. Conf. on Data Mining. (2001), 241-248.
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- [12] K. Nozaki, H. Ishibuchi and H. Tanaka: A simple but powerful heuristic method for generating fuzzy rules from numerical data. Fuzzy Sets and Systems, 86(3), (1997), 251-270.
- [13] J. R. Quinlan: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo, 1993.
- [14] S. J. Russell and P. Norvig: Artificial Intelligence: A Modern Approach. Prentice-Hall, Upper Saddle River, 1995.
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Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BSW3-0003-0002