標題: A fuzzy inductive learning strategy for modular rules
作者: Wang, CH
Liu, JF
Hong, TP
Tseng, SS
資訊工程學系
Department of Computer Science
關鍵字: expert systems;fuzzy machine learning;fuzzy sets;knowledge acquisition;measure of fuzziness;membership functions
公開日期: 1-Apr-1999
摘要: In real applications, data provided to a learning system usually contain linguistic information which greatly influences concept descriptions derived by conventional inductive learning methods. The design of learning methods to learn concept descriptions in working with vague data is thus very important. In this paper, we apply fuzzy set concepts to machine learning to solve this problem. A fuzzy learning algorithm based on the maximum information gain is proposed to manage linguistic information. The proposed learning algorithm generates fuzzy rules from "soft" instances, which differ from conventional instances in that they have class membership values. Experiments on the Sports and the Iris Flower classification problems are presented to compare the accuracy of the proposed algorithm with those of some other learning algorithms. Experimental results show that the rules derived from our approach are simpler and yield higher accuracy than those from some other learning algorithms. (C) 1999 Elsevier Science B.V. All rights reserved.
URI: http://hdl.handle.net/11536/31429
ISSN: 0165-0114
期刊: FUZZY SETS AND SYSTEMS
Volume: 103
Issue: 1
起始頁: 91
結束頁: 105
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