Gaspar-Cunha A, Recio G, Costa L, Estébanez C
Institute of Polymers and Composites-I3N, University of Minho, Guimarães, Portugal.
Department of Computer Science, Universidad Carlos III de Madrid, Leganes, Madrid, Spain.
ScientificWorldJournal. 2014 Feb 23;2014:314728. doi: 10.1155/2014/314728. eCollection 2014.
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.
破产预测是金融和会计领域的一个广阔范畴，其重要性在于对债权人和投资者评估陷入破产可能性的相关性。随着公司变得复杂，它们会制定复杂的方案来掩盖其真实状况。相应地，评估与交易对手相关的信用风险或预测破产变得更加困难。进化算法已被证明是处理金融和经济领域复杂问题的优秀工具，这些领域涉及大量不相关特征。本文提供了一种使用进化多目标方法对破产数据集进行分类时的特征选择方法，该方法同时最小化特征数量并最大化分类器质量度量（例如，准确率）。所提出的方法通过应用特征选择算法同时优化所使用分类器的参数来利用自适应。该方法应用于四组不同的数据。所得结果显示了使用分类器自适应的效用。
