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

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

A. Gaspar-Cunha, F. Mendes, J. Duarte, A. Vieira, B. Ribeiro, A. Ribeiro, J. Neves
Copyright: © 2012 |Pages: 21
ISBN13: 9781466615748|ISBN10: 1466615745|EISBN13: 9781466615755
DOI: 10.4018/978-1-4666-1574-8.ch009
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MLA

Gaspar-Cunha, A., et al. "Feature Selection for Bankruptcy Prediction: A Multi-Objective Optimization Approach." Nature-Inspired Computing Design, Development, and Applications, edited by Leandro Nunes de Castro, IGI Global, 2012, pp. 158-178. https://doi.org/10.4018/978-1-4666-1574-8.ch009

APA

Gaspar-Cunha, A., Mendes, F., Duarte, J., Vieira, A., Ribeiro, B., Ribeiro, A., & Neves, J. (2012). Feature Selection for Bankruptcy Prediction: A Multi-Objective Optimization Approach. In L. Nunes de Castro (Ed.), Nature-Inspired Computing Design, Development, and Applications (pp. 158-178). IGI Global. https://doi.org/10.4018/978-1-4666-1574-8.ch009

Chicago

Gaspar-Cunha, A., et al. "Feature Selection for Bankruptcy Prediction: A Multi-Objective Optimization Approach." In Nature-Inspired Computing Design, Development, and Applications, edited by Leandro Nunes de Castro, 158-178. Hershey, PA: IGI Global, 2012. https://doi.org/10.4018/978-1-4666-1574-8.ch009

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Abstract

In this work a Multi-Objective Evolutionary Algorithm (MOEA) was applied for feature selection in the problem of bankruptcy prediction. This algorithm maximizes the accuracy of the classifier while keeping the number of features low. A two-objective problem, that is minimization of the number of features and accuracy maximization, was fully analyzed using the Logistic Regression (LR) and Support Vector Machines (SVM) classifiers. Simultaneously, the parameters required by both classifiers were also optimized, and 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 proposed method can provide useful information for decision makers in characterizing the financial health of a company.

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