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dc.contributor.authorCharalambous, Chrisen
dc.contributor.authorCharitou, Andreasen
dc.contributor.authorKaourou, Frosoen
dc.coverage.spatialPiscataway, NJ, United Statesen
dc.creatorCharalambous, Chrisen
dc.creatorCharitou, Andreasen
dc.creatorKaourou, Frosoen
dc.date.accessioned2019-04-24T06:29:23Z
dc.date.available2019-04-24T06:29:23Z
dc.date.issued2000
dc.identifier.urihttp://gnosis.library.ucy.ac.cy/handle/7/46689
dc.description.abstractThis study uses the feature selection algorithm proposed by Setiono and Liu to select the most relevant features for the bankruptcy prediction problem. The method uses a feedforward neural network with one hidden layer to decide which features to be removed. Our data consists of 139 matched pair of bankrupt and nonbankrupt U.S. firms for the period 1983-1994. The results of this study indicate that the final neural network obtained with reduced number of inputs gives significantly better prediction results than the one that uses all initial features.en
dc.language.isoengen
dc.publisherIEEEen
dc.sourceProceedings of the International Joint Conference on Neural Networksen
dc.subjectAlgorithmsen
dc.subjectBankruptcy predictionen
dc.subjectFeature extractionen
dc.subjectFeature selection algorithmsen
dc.subjectFeedforward neural networksen
dc.titleApplication of feature extractive algorithm to bankruptcy predictionen
dc.typeinfo:eu-repo/semantics/conferenceObject
dc.description.volume5
dc.description.startingpage303
dc.description.endingpage308
dc.author.facultyΣχολή Οικονομικών Επιστημών και Διοίκησης / Faculty of Economics and Management
dc.author.departmentΤμήμα Λογιστικής και Χρηματοοικονομικής / Department of Accounting and Finance
dc.type.uhtypeConference Objecten
dc.contributor.orcidCharitou, Andreas [0000-0003-1080-9121]
dc.description.totalnumpages303-308
dc.gnosis.orcid0000-0003-1080-9121


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