Feature Selection for Bankruptcy Prediction: A Multi-Objective Optimization Approach
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.
Unable to display preview. Download preview PDF.
- 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
- 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
- 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.Gaspar-Cunha, A.: Modelling and Optimization of Single Screw Extrusion. PhD Thesis, University of Minho, Guimarães, Portugal (2000), http://www.lania.mx/~ccoello/EMOO/