Conference Paper

Cost-Sensitive Learning Vector Quantization for Financial Distress Prediction

DOI: 10.1007/978-3-642-04686-5_31 Conference: Progress in Artificial Intelligence, 14th Portuguese Conference on Artificial Intelligence, EPIA 2009, Aveiro, Portugal, October 12-15, 2009. Proceedings
Source: DBLP


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.

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