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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    ABSTRACT: The importance of cost-sensitive learning becomes crucial when the costs of misclassifications are quite different. Many evidences have demonstrated that a cost-sensitive predictive model is more desirable in practical applications than a traditional one without taking the cost into consideration. In this paper, we propose two approaches which incorporate the cost matrix into original learning vector quantization by means of instance weighting. Empirical results show that the proposed algorithms are effective on both binary-class data and multi-class data.
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    ABSTRACT: Cost-sensitive classification algorithms that enable effective prediction, where the costs of misclassification can be very different, are crucial to creditors and auditors in credit risk analysis. Learning vector quantization (LVQ) is a powerful tool to solve bankruptcy prediction problem as a classification task. The genetic algorithm (GA) is applied widely in conjunction with artificial intelligent methods. The hybridization of genetic algorithm with existing classification algorithms is well illustrated in the field of bankruptcy prediction. In this paper, a hybrid GA and LVQ approach is proposed to minimize the expected misclassified cost under the asymmetric cost preference. Experiments on real-life French private company data show the proposed approach helps to improve the predictive performance in asymmetric cost setup.
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    ABSTRACT: Credit rating is involved in many financial applications to estimate the creditworthiness of corporations or individuals. In addition to building accurate credit rating models, the stability of models is of significant importance to economic performance. In this work we propose a methodology based on learning vector quantization (LVQ) to develop a credit rating model. This model is applied to a French database of private companies over a period of several years. LVQ is trained and calibrated in a supervised way using data from 2006 and then applied to the remaining years. We analyze one year transition matrix and show that the model is capable to create robust and stable classes to rank companies.
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