Cost-Sensitive Learning Vector Quantization for Financial Distress Prediction
ABSTRACT 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.
SourceAvailable from: Armando Vieira[Show abstract] [Hide abstract]
ABSTRACT: Bankruptcy trajectory reflects the dynamic changes of financial situation of companies, and hence make possible to keep track of the evolution of companies and recognize the important trajectory patterns. This study aims at a compact visualization of the complex temporal behaviors in financial statements. We use self-organizing map (SOM) to analyze and visualize the financial situation of companies over several years through a two-step clustering process. Initially, the bankruptcy risk is characterized by a feature self-organizing map (FSOM), and therefore the temporal sequence is converted to the trajectory vector projected on the map. Afterwards, the trajectory self-organizing map (TSOM) clusters the trajectory vectors to a number of trajectory patterns. The proposed approach is applied to a large database of French companies spanning over four years. The experimental results demonstrate the promising functionality of SOM for bankruptcy trajectory clustering and visualization. From the viewpoint of decision support, the method might give experts insight into the patterns of bankrupt and healthy company development.Expert Systems with Applications 01/2013; 40(1):385–393. DOI:10.1016/j.eswa.2012.07.047 · 1.97 Impact Factor
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ABSTRACT: Skewed class distribution and non-uniform misclassification cost are pervasive in many real-world domains such as bankruptcy prediction, medical diagnosis, and intrusion detection. Although class imbalance learning and cost-sensitive learning can be manipulated in a unified framework as was illustrated in previous studies, the influence of class distribution on cost-sensitive learning still needs clarification. In this paper, we investigate the effect of cost ratio, imbalance ratio and sample size on classification performance using a real-world French bankruptcy database. The results show that the cost ratio and the level of class imbalance have strong effect on prediction performance. A near-balanced training data set is favorable when a relatively uniform cost ratio is used, whereas a near-natural class distribution is favorable when a highly uneven cost ratio is used.Intelligent Data Analysis 05/2013; 17(3):423-437. DOI:10.3233/IDA-130587 · 0.50 Impact Factor
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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.