Identifying the Impact of Decision Variables for Nonlinear Classification Tasks

Cited 13 time in webofscience Cited 0 time in scopus
  • Hit : 318
  • Download : 0
DC FieldValueLanguage
dc.contributor.authorKim, Steven H.ko
dc.contributor.authors.w. shinko
dc.date.accessioned2013-03-02T16:56:06Z-
dc.date.available2013-03-02T16:56:06Z-
dc.date.created2012-02-06-
dc.date.created2012-02-06-
dc.date.issued2000-04-
dc.identifier.citationEXPERT SYSTEMS WITH APPLICATIONS, v.18, no.3, pp.201 - 214-
dc.identifier.issn0957-4174-
dc.identifier.urihttp://hdl.handle.net/10203/74560-
dc.description.abstractThis paper presents a novel procedure to improve a class of learning systems known as lazy learning algorithms by optimizing the selection of variables and their attendant weights through an artificial neural network and a genetic algorithm. The procedure utilizes its previous knowledge base-also called a case base-to select an effective subset for adaptation. In particular, the procedure explores a space of N variables and generates a reduced space of M dimensions. This is achieved through clustering and compaction. The clustering stage involves the minimization of distances among individuals within the same class while maximizing the distances among different classes. The compaction stage involves the elimination of the irrelevant or redundant feature dimensions. To achieve these two goals concurrently through the evolutionary process, new measures of fitness have been developed. The metrics lead to procedures which exhibit superior characteristics in terms of both accuracy and efficiency. The efficiency springs from a reduction in the number of features required for analysis, thereby saving on computational cost as well as data collection requirements. The utility of the new techniques is validated against a variety of data sets from natural and commercial sources. (C) 2000 Published by Elsevier Science Ltd. All rights reserved.-
dc.languageEnglish-
dc.publisherPergamon-Elsevier Science Ltd-
dc.subjectLEARNING ALGORITHMS-
dc.subjectGENETIC ALGORITHMS-
dc.subjectNEURAL NETWORKS-
dc.subjectSELECTION-
dc.subjectFEATURES-
dc.titleIdentifying the Impact of Decision Variables for Nonlinear Classification Tasks-
dc.typeArticle-
dc.identifier.wosid000086807300003-
dc.identifier.scopusid2-s2.0-0034171891-
dc.type.rimsART-
dc.citation.volume18-
dc.citation.issue3-
dc.citation.beginningpage201-
dc.citation.endingpage214-
dc.citation.publicationnameEXPERT SYSTEMS WITH APPLICATIONS-
dc.contributor.localauthorKim, Steven H.-
dc.contributor.nonIdAuthors.w. shin-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorfeature weighting-
dc.subject.keywordAuthorsimilarity assessment-
dc.subject.keywordAuthork-nearest neighbor-
dc.subject.keywordAuthorlazy learning-
dc.subject.keywordAuthorartificial neural network-
dc.subject.keywordAuthorgenetic algorithms-
dc.subject.keywordPlusLEARNING ALGORITHMS-
dc.subject.keywordPlusGENETIC ALGORITHMS-
dc.subject.keywordPlusNEURAL NETWORKS-
dc.subject.keywordPlusSELECTION-
dc.subject.keywordPlusFEATURES-
Appears in Collection
RIMS Journal Papers
Files in This Item
There are no files associated with this item.
This item is cited by other documents in WoS
⊙ Detail Information in WoSⓡ Click to see webofscience_button
⊙ Cited 13 items in WoS Click to see citing articles in records_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0