Feature Selection for Bankruptcy Prediction: A Multi-Objective Optimization Approach

  • Fernando Mendes
  • João Duarte
  • Armando Vieira
  • António Gaspar-Cunha
Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 75)

Abstract

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.

Keywords

Support Vector Machine Feature Selection Linear Discriminant Analysis Pareto Front Credit Union 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Fernando Mendes
    • 1
  • João Duarte
    • 2
  • Armando Vieira
    • 2
  • António Gaspar-Cunha
    • 1
  1. 1.IPC/I3N - Institute of Polymers and CompositesUniversity of MinhoGuimarãesPortugal
  2. 2.Department of PhysicsInstituto Superior de Engenharia do PortoPortoPortugal

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