Publicação

Feature selection for bankruptcy prediction: a multi-objective optimization approach

Ver documento

Detalhes bibliográficos
Resumo: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 – is fully analyzed using two classifiers: Support Vector Machines and Logistic Function. A database containing financial statements of 1200 medium sized private French companies was used. It was shown that MOEA is a very efficient feature selection approach. Furthermore, it can provide very useful information for the decision maker in characterizing the financial health of a company.
Autores principais:Mendes, F.
Outros Autores:Duarte, J.; Vieira, Armando; Gaspar-Cunha, A.
Assunto:Feature selection Bankruptcy prediction Multi-objective optimization Evolutionary algorithms Support vector machines Logistic regression
Ano:2010
País:Portugal
Tipo de documento:comunicação em conferência
Tipo de acesso:acesso aberto
Instituição associada:Universidade do Minho
Idioma:inglês
Origem:RepositóriUM - Universidade do Minho
Descrição
Resumo: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 – is fully analyzed using two classifiers: Support Vector Machines and Logistic Function. A database containing financial statements of 1200 medium sized private French companies was used. It was shown that MOEA is a very efficient feature selection approach. Furthermore, it can provide very useful information for the decision maker in characterizing the financial health of a company.