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 es...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/314728 |
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| author | A. Gaspar-Cunha G. Recio L. Costa C. Estébanez |
| author_facet | A. Gaspar-Cunha G. Recio L. Costa C. Estébanez |
| author_sort | A. Gaspar-Cunha |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-a788a095256f46828ad458bdd2f24c53 |
| institution | OA Journals |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-a788a095256f46828ad458bdd2f24c532025-08-20T02:20:57ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/314728314728Self-Adaptive MOEA Feature Selection for Classification of Bankruptcy Prediction DataA. Gaspar-Cunha0G. Recio1L. Costa2C. Estébanez3Institute of Polymers and Composites-I3N, University of Minho, Guimarães, PortugalDepartment of Computer Science, Universidad Carlos III de Madrid, Leganes, Madrid, SpainDepartment of Production and Systems Engineering, University of Minho, Braga, PortugalDepartment of Computer Science, Universidad Carlos III de Madrid, Leganes, Madrid, SpainBankruptcy 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.http://dx.doi.org/10.1155/2014/314728 |
| spellingShingle | A. Gaspar-Cunha G. Recio L. Costa C. Estébanez Self-Adaptive MOEA Feature Selection for Classification of Bankruptcy Prediction Data The Scientific World Journal |
| title | Self-Adaptive MOEA Feature Selection for Classification of Bankruptcy Prediction Data |
| title_full | Self-Adaptive MOEA Feature Selection for Classification of Bankruptcy Prediction Data |
| title_fullStr | Self-Adaptive MOEA Feature Selection for Classification of Bankruptcy Prediction Data |
| title_full_unstemmed | Self-Adaptive MOEA Feature Selection for Classification of Bankruptcy Prediction Data |
| title_short | Self-Adaptive MOEA Feature Selection for Classification of Bankruptcy Prediction Data |
| title_sort | self adaptive moea feature selection for classification of bankruptcy prediction data |
| url | http://dx.doi.org/10.1155/2014/314728 |
| work_keys_str_mv | AT agasparcunha selfadaptivemoeafeatureselectionforclassificationofbankruptcypredictiondata AT grecio selfadaptivemoeafeatureselectionforclassificationofbankruptcypredictiondata AT lcosta selfadaptivemoeafeatureselectionforclassificationofbankruptcypredictiondata AT cestebanez selfadaptivemoeafeatureselectionforclassificationofbankruptcypredictiondata |