Bankruptcy prediction using case-based reasoning, neural networks, and discriminant analysis

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dc.contributor.authorJo H.-
dc.contributor.authorHan, Ingoo-
dc.contributor.authorLee H.-
dc.date.available2008-04-11T02:17:40Z-
dc.date.created2012-02-06-
dc.date.issued1997-08-
dc.identifier.citationEXPERT SYSTEMS WITH APPLICATIONS, v.13, no.2, pp.97 - 108-
dc.identifier.issn0957-4174-
dc.identifier.urihttp://hdl.handle.net/10203/3782-
dc.description.abstractBankruptcy prediction is one of the major business classification problems. In this paper, we use three different techniques: (1) Multivariate discriminant analysis, (2) case-based forecasting, and (3) neural network to predict Korean bankrupt and nonbankrupt firms. The average hit ratios of three methods range from 81.5 to 83.8%. Neural network performs better than discriminant analysis and the case-based forecasting system. ? 1997 Elsevier Science Ltd.-
dc.languageENG-
dc.language.isoen_USen
dc.publisherPergamon Press Ltd.-
dc.titleBankruptcy prediction using case-based reasoning, neural networks, and discriminant analysis-
dc.typeArticle-
dc.identifier.scopusid2-s2.0-0031199479-
dc.type.rimsART-
dc.citation.volume13-
dc.citation.issue2-
dc.citation.beginningpage97-
dc.citation.endingpage108-
dc.citation.publicationnameEXPERT SYSTEMS WITH APPLICATIONS-
dc.contributor.localauthorHan, Ingoo-
dc.contributor.nonIdAuthorJo H.-
dc.contributor.nonIdAuthorLee H.-
dc.type.journalArticleArticle-
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