Finding relevant attributes and membership functions

被引:104
作者
Hong, TP [1 ]
Chen, JB
机构
[1] Kaohsiung Polytech Inst, Dept Informat Management, Kaohsiung 84008, Taiwan
[2] Kaohsiung Polytech Inst, Inst Management Sci, Kaohsiung 84008, Taiwan
关键词
fuzzy if-then rule; membership function; relevant attribute; knowledge acquisition; machine learning;
D O I
10.1016/S0165-0114(97)00187-5
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Fuzzy systems that automatically derive fuzzy if-then rules from numeric data have been developed Most have to predefine membership functions in order to learn. Hong and Lee proposed a general learning method that automatically derives fuzzy if-then rules and membership functions from a set of given training examples using a decision table. All available attributes were included in the decision table and the initial membership functions for each attribute were built according to the predefined smallest unit. Although Hong and Lee's method accurately derives the fuzzy if-then rules and final membership functions, the decision table and the initial membership functions are complex if there are many attributes or if the predefined unit is small. We improve Hong and Lee's method by first selecting relevant attributes and building appropriate initial membership functions. These attributes and membership functions are then used in a decision table to derive final fuzzy if-then rules and membership functions. Experimental results on Iris data show that the proposed method effectively induces membership functions and fuzzy if-then rules. (C) 1999 Elsevier Science B.V. All rights reserved.
引用
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页码:389 / 404
页数:16
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