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dc.contributor.authorGaspar-Cunha, A.-
dc.contributor.authorMendes, F.-
dc.contributor.authorDuarte, J.-
dc.contributor.authorVieira, Armando-
dc.contributor.authorRibeiro, Bernardete-
dc.contributor.authorRibeiro, André M. S.-
dc.contributor.authorNeves, João Carvalho-
dc.description.abstractIn this work a Multi-Objective Evolutionary Algorithm (MOEA) was applied for feature selection in the problem of bankruptcy prediction. The aim is to maximize the accuracy of the classifier while keeping the number of features low. A two-objective problem - minimization of the number of features and accuracy maximization – was fully analyzed using two classifiers, Logistic Regression (LR) and Support Vector Machines (SVM). Simultaneously, the parameters required by both classifiers were also optimized. The validity of the methodology proposed was tested using a database containing financial statements of 1200 medium sized private French companies. Based on extensive tests it is shown that MOEA is an efficient feature selection approach. Best results were obtained when both the accuracy and the classifiers parameters are optimized. The method proposed can provide useful information for the decision maker in characterizing the financial health of a company.por
dc.publisherIGI Globalpor
dc.subjectFeature selectionpor
dc.subjectBankruptcy predictionpor
dc.subjectMulti-objective optimizationpor
dc.subjectEvolutionary algorithmspor
dc.subjectSupport vector machinespor
dc.subjectLogistic regressionpor
dc.titleFeature selection for bankruptcy prediction: a multi-objective optimization approachpor
sdum.bookTitleNature-Inspired Computing Design, Development, and Applications-
Appears in Collections:IPC - Artigos em revistas científicas internacionais com arbitragem

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