Soft Computing in Industrial Applications pp 109-115
Feature Selection for Bankruptcy Prediction: A Multi-Objective Optimization Approach
- Cite this paper as:
- Mendes F., Duarte J., Vieira A., Gaspar-Cunha A. (2010) Feature Selection for Bankruptcy Prediction: A Multi-Objective Optimization Approach. In: Gao XZ., Gaspar-Cunha A., Köppen M., Schaefer G., Wang J. (eds) Soft Computing in Industrial Applications. Advances in Intelligent and Soft Computing, vol 75. Springer, Berlin, Heidelberg
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.
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