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    Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/4711


    Title: A fuzzy inductive learning strategy for modular rules
    Authors: C. H. Wang;J. F. Liu;T. P. Hong;S. S. Tseng
    Contributors: Department of Information Science and Applications
    Date: 1999-04
    Issue Date: 2009-11-30 16:03:13 (UTC+8)
    Publisher: Asia University
    Abstract: 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.
    Relation: Fuzzy Sets and System 103(1):91-105
    Appears in Collections:[行動商務與多媒體應用學系] 期刊論文

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