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dc.contributor.authorGüvenir, H. A.en_US
dc.contributor.authorÇakır, M.en_US
dc.date.accessioned2016-02-08T09:59:53Z
dc.date.available2016-02-08T09:59:53Z
dc.date.issued2010en_US
dc.identifier.issn0957-4174
dc.identifier.urihttp://hdl.handle.net/11693/22420
dc.description.abstractVoting features based classifiers, shortly VFC, have been shown to perform well on most real-world data sets. They are robust to irrelevant features and missing feature values. In this paper, we introduce an extension to VFC, called voting features based classifier with feature construction, VFCC for short, and show its application to the problem of predicting if a bank will encounter financial distress, by analyzing current financial statements. The previously developed VFC learn a set of rules that contain a single condition based on a single feature in their antecedent. The VFCC algorithm proposed in this work, on the other hand, constructs rules whose antecedents may contain conjuncts based on several features. Experimental results on recent financial ratios of banks in Turkey show that the VFCC algorithm achieves better accuracy than other well-known rule learning classification algorithms. © 2009 Elsevier Ltd. All rights reserved.en_US
dc.language.isoEnglishen_US
dc.source.titleExpert Systems with Applications: an international journalen_US
dc.relation.isversionofhttp://dx.doi.org/10.1016/j.eswa.2009.06.037en_US
dc.subjectClassificationen_US
dc.subjectFeature constructionen_US
dc.subjectFeature projectionsen_US
dc.subjectFinancial distressen_US
dc.subjectVotingen_US
dc.subjectClassification algorithmen_US
dc.subjectClassification featuresen_US
dc.titleVoting features based classifier with feature construction and its application to predicting financial distressen_US
dc.typeArticleen_US
dc.departmentDepartment of Computer Engineeringen_US
dc.citation.spage1713en_US
dc.citation.epage1718en_US
dc.citation.volumeNumber37en_US
dc.citation.issueNumber2en_US
dc.identifier.doi10.1016/j.eswa.2009.06.037en_US
dc.publisherPergamon Pressen_US


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