Self-adaptive MOEA feature selection for classification of bankruptcy prediction data.
Bankruptcy prediction is a vast area of finance and accounting whose importance lies in the relevance for creditors and investors in evaluating the likelihood of getting into bankrupt. As companies become complex, they develop sophisticated schemes to hide their real situation. In turn, making an estimation of the credit risks associated with counterparts or predicting bankruptcy becomes harder. Evolutionary algorithms have shown to be an excellent tool to deal with complex problems in finances and economics where a large number of irrelevant features are involved. This paper provides a methodology for feature selection in classification of bankruptcy data sets using an evolutionary multiobjective approach that simultaneously minimise the number of features and maximise the classifier quality measure (e.g., accuracy). The proposed methodology makes use of self-adaptation by applying the feature selection algorithm while simultaneously optimising the parameters of the classifier used. The methodology was applied to four different sets of data. The obtained results showed the utility of using the self-adaptation of the classifier.
Institute of Polymers and Composites-I3N, University of Minho, Guimarães, Portugal.
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Gaspar-Cunha
Department of Computer Science, Universidad Carlos III de Madrid, Leganes, Madrid, Spain.
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Recio
Department of Production and Systems Engineering, University of Minho, Braga, Portugal.
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Costa
Department of Computer Science, Universidad Carlos III de Madrid, Leganes, Madrid, Spain.
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Estébanez
D000465:Algorithms; D016361:Bankruptcy / Q000145:classification* / Q000639:trends; D066264:Datasets as Topic / Q000145:classification* / Q000639:trends; D005544:Forecasting; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods* / Q000639:trends