Effect of fuzzy discretization in fuzzy rule-based systems for classification problems with continuous attributes

• Autorzy: Ishibuchi H. , Yamamoto T.
• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

pattern classification; discretization of continuous attributes; rule extraction; data mining; fuzzy rules; rule weights

• Wydawca: Polish Academy of Sciences, Committee of Automatic Control and Robotics
• Czasopismo: Archives of Control Sciences
• Rocznik: 2002
• Tom: Vol. 12, no. 4
• Strony: 351--377
• Opis fizyczny: Bibliogr. 15 poz., rys.

Twórcy

autor

Ishibuchi H.

• Department of Industrial Engineering, Osaka Prefecture University, Sakai 599-8531 Japan

autor

Yamamoto T.

Bibliografia

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