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

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


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.


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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Altman, E.: Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance 23, 589–609 (1968)CrossRefGoogle Scholar
  2. 2.
    Nanda, S., Pendharkar, P.: Linear models for minimizing misclassification costs in bankruptcy prediction. Intelligent Systems in Accounting, Finance & Management 10, 155–168 (2001)CrossRefGoogle Scholar
  3. 3.
    Charitou, A., Neophytou, E., Charalambous, C.: Predicting Corporate Failure: Empirical Evidence for the UK. European Accounting Review 13, 465–497 (2004)CrossRefGoogle Scholar
  4. 4.
    Atiya, A.F.: Bankruptcy prediction for credit risk using neural networks A survey and new results. IEEE Transactions on Neural Networks 12, 929–935 (2001)CrossRefGoogle Scholar
  5. 5.
    Coats, P.K., Fant, L.F.: Recognizing Financial Distress Patterns Using a Neural Network Tool. Financial Management 22, 142–155 (1993)CrossRefGoogle Scholar
  6. 6.
    Yang, Z.R.: Probabilistic Neural Networks in bankruptcy prediction. Journal of Business Research 44, 67–75 (1999)CrossRefGoogle Scholar
  7. 7.
    Tan, C., Dihardjo, H.: A study on using artificial neural networks to develop an early warning predictor for credit union financial distress with comparison to the probit model. Managerial Finance 27(4), 56–77 (2001)CrossRefGoogle Scholar
  8. 8.
    Alfaro-Cid, E., Castillo, P.A., Esparcia, A., Sharman, K., Merelo, J.J., Prieto, A., Mora, A.M., Laredo, J.L.J.: Comparing Multiobjective Evolutionary Ensembles for Minimizing Type I and II Errors for Bankruptcy Prediction. In: 2008 Congress on Evolutionary Computation (CEC 2008), pp. 2907–2913 (2008)Google Scholar
  9. 9.
    Hamdani, T.M., Won, J.-M., Alimi, A.M., Karray, F.: Multi-objective Feature Selection with NSGA II. In: Beliczynski, B., Dzielinski, A., Iwanowski, M., Ribeiro, B. (eds.) ICANNGA 2007. Part I, LNCS, vol. 4431, pp. 240–247. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  10. 10.
    Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Transaction on Evolutionary Computation 6(2), 181–197 (2002)CrossRefGoogle Scholar
  11. 11.
    Agresti, A.: Categorical Data Analysis. Wiley-Interscience, New York (2002)zbMATHCrossRefGoogle Scholar
  12. 12.
    Vapnik, V.: The Nature of Statistical Learning Theory. Springer, Heidelberg (1995)zbMATHGoogle Scholar
  13. 13.
    Gaspar-Cunha, A., Oliveira, P., Covas, J.A.: Use of Genetic Algorithms in Multicriteria Optimization to Solve Industrial Problems. In: Seventh Int. Conf. on Genetic Algorithms, Michigan, USA (1997)Google Scholar
  14. 14.
    Deb, K.: Multi-Objective Optimization using Evolutionary Algorithms. Wiley, Chichester (2001)zbMATHGoogle Scholar
  15. 15.
    Gaspar-Cunha, A., Covas, J.A.: RPSGAe - A Multiobjective Genetic Algorithm with Elitism: Application to Polymer Extrusion. In: Gandibleux, X., Sevaux, M., Sörensen, K., T’kindt, V. (eds.) Metaheuristics for Multiobjective Optimisation. LNCS, vol. 535, pp. 221–249. Springer, Heidelberg (2004)Google Scholar
  16. 16.
    Gaspar-Cunha, A.: Modelling and Optimization of Single Screw Extrusion. PhD Thesis, University of Minho, Guimarães, Portugal (2000),
  17. 17.
    da Fonseca, V.G., Fonseca, C., Hall, A.: Inferential performance assessment of stochastic optimisers and the attainment function. In: Ziztler, E., Deb, K., Thiele, L., Coello Coello, C.A., Corne, D. (eds.) EMO 2001. LNCS, vol. 1993, pp. 213–225. Springer, Heidelberg (2001)CrossRefGoogle Scholar

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

Personalised recommendations