Cost-Sensitive Learning Vector Quantization for Financial Distress Prediction

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.

[1]  Zhi-Hua Zhou,et al.  The Influence of Class Imbalance on Cost-Sensitive Learning: An Empirical Study , 2006, Sixth International Conference on Data Mining (ICDM'06).

[2]  M. Maloof.  Learning When Data Sets are Imbalanced and When Costs are Unequal and Unknown , 2003 .

[3]  Sudhir Nanda,et al.  Linear models for minimizing misclassification costs in bankruptcy prediction , 2001, Intell. Syst. Account. Finance Manag..

[4]  Bianca Zadrozny,et al.  Learning and making decisions when costs and probabilities are both unknown , 2001, KDD '01.

[5]  Sudhir Nanda,et al.  A misclassification cost-minimizing evolutionary–neural classification approach , 2006 .

[6]  Teuvo Kohonen,et al.  Self-Organizing Maps , 2010 .

[7]  Rimvydas Simutis,et al.  FORECASTING OF CREDIT CLASSES WITH THE SELF-ORGANIZING MAPS , 2004 .

[8]  Zhi-Hua Zhou,et al.  Ieee Transactions on Knowledge and Data Engineering 1 Training Cost-sensitive Neural Networks with Methods Addressing the Class Imbalance Problem , 2022 .

[9]  Ömer Kaan Baykan,et al.  Predicting bank financial failures using neural networks, support vector machines and multivariate statistical methods: A comparative analysis in the sample of savings deposit insurance fund (SDIF) transferred banks in Turkey , 2009, Expert Syst. Appl..

[10]  Ning Chen,et al.  A Batch Learning Vector Quantization Algorithm for Categorical Data , 2009, ICAART.

[11]  Parag C. Pendharkar,et al.  A threshold varying bisection method for cost sensitive learning in neural networks , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..

[12]  Hussein A. Abdou.  Genetic programming for credit scoring: The case of Egyptian public sector banks , 2009, Expert Syst. Appl..

[13]  Janez Demsar,et al.  Statistical Comparisons of Classifiers over Multiple Data Sets , 2006, J. Mach. Learn. Res..

[14]  Chulwoo Jeong,et al.  A binary classification method for bankruptcy prediction , 2009, Expert Syst. Appl..

[15]  Qiang Yang,et al.  Decision trees with minimal costs , 2004, ICML.

[16]  Johannes Grotendorst,et al.  Classification of Highly Unbalanced CYP450 Data of Drugs Using Cost Sensitive Machine Learning Techniques , 2007, J. Chem. Inf. Model..

[17]  Vadlamani Ravi,et al.  Bankruptcy prediction in banks and firms via statistical and intelligent techniques - A review , 2007, Eur. J. Oper. Res..

[18]  Yang Wang,et al.  Cost-sensitive boosting for classification of imbalanced data , 2007, Pattern Recognit..

[19]  David West,et al.  Neural network credit scoring models , 2000, Comput. Oper. Res..

[20]  Sungzoon Cho,et al.  Application of LVQ to novelty detection using outlier training data , 2006, Pattern Recognit. Lett..

[21]  Tian-Shyug Lee,et al.  A two-stage hybrid credit scoring model using artificial neural networks and multivariate adaptive regression splines , 2005, Expert Syst. Appl..

[22]  Kai Ming Ting,et al.  An Instance-weighting Method to Induce Cost-sensitive Trees , 2001 .

[23]  Armando Vieira,et al.  Improving bankruptcy prediction with Hidden Layer Learning Vector Quantization , 2006 .

[24]  Ning Chen,et al.  Bankruptcy Prediction based on Independent Component Analysis , 2009, ICAART.

[25]  Jacek M. Zurada,et al.  Artificial Intelligence and Soft Computing - ICAISC 2008, 9th International Conference, Zakopane, Poland, June 22-26, 2008, Proceedings , 2008, ICAISC.

[26]  David E. Booth,et al.  A comparison of supervised and unsupervised neural networks in predicting bankruptcy of Korean firms , 2005, Expert Syst. Appl..

[27]  Yonggwan Won,et al.  Classification of Unbalanced Medical Data with Weighted Regularized Least Squares , 2007, 2007 Frontiers in the Convergence of Bioscience and Information Technologies.

[28]  Petr Hájek,et al.  Municipal Creditworthiness Modelling by Kohonen's Self-organizing Feature Maps and LVQ Neural Networks , 2006, ICAISC.