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TitleFeature selection for bankruptcy prediction: a multi-objective optimization approach
Author(s)Gaspar-Cunha, A.
Mendes, F.
Duarte, J.
Vieira, Armando
Ribeiro, Bernardete
Ribeiro, André M. S.
Neves, João Carvalho
KeywordsFeature selection
Bankruptcy prediction
Multi-objective optimization
Evolutionary algorithms
Support vector machines
Logistic regression
Issue date2012
PublisherIGI Global
Abstract(s)In 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.
TypeBook part
Publisher version
AccessOpen access
Appears in Collections:IPC - Artigos em revistas científicas internacionais com arbitragem

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