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Published in:
2009 | OriginalPaper | Chapter
Cost-Sensitive Learning Vector Quantization for Financial Distress Prediction
Authors : Ning Chen, Armando S. Vieira, João Duarte, Bernardete Ribeiro, João C. Neves
Published in: Progress in Artificial Intelligence
Publisher: Springer Berlin Heidelberg
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Financial distress prediction is of crucial importance in credit risk analysis with the increasing competition and complexity of credit industry. Although a variety of methods have been applied in this field, there are still some problems remained. The accurate and sensitive prediction in presence of unequal misclassification costs is an important one. Learning vector quantization (LVQ) is a powerful tool to solve financial distress prediction problem as a classification task. In this paper, a cost-sensitive version of LVQ is proposed which incorporates the cost information in the model. Experiments on two real data sets show the proposed approach is effective to improve the predictive capability in cost-sensitive situation.