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dc.contributor.authorWang, CHen_US
dc.contributor.authorLiu, JFen_US
dc.contributor.authorHong, TPen_US
dc.contributor.authorTseng, SSen_US
dc.date.accessioned2014-12-08T15:46:44Z-
dc.date.available2014-12-08T15:46:44Z-
dc.date.issued1999-04-01en_US
dc.identifier.issn0165-0114en_US
dc.identifier.urihttp://hdl.handle.net/11536/31429-
dc.description.abstractIn 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.en_US
dc.language.isoen_USen_US
dc.subjectexpert systemsen_US
dc.subjectfuzzy machine learningen_US
dc.subjectfuzzy setsen_US
dc.subjectknowledge acquisitionen_US
dc.subjectmeasure of fuzzinessen_US
dc.subjectmembership functionsen_US
dc.titleA fuzzy inductive learning strategy for modular rulesen_US
dc.typeArticleen_US
dc.identifier.journalFUZZY SETS AND SYSTEMSen_US
dc.citation.volume103en_US
dc.citation.issue1en_US
dc.citation.spage91en_US
dc.citation.epage105en_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.identifier.wosnumberWOS:000078726700007-
dc.citation.woscount26-
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