Relevance vector machine based infinite decision agent ensemble learning for credit risk analysis

In this paper, a relevance vector machine based infinite decision agent ensemble learning (RVM"I"d"e"a"l) system is proposed for the robust credit risk analysis. In the first level of our model, we adopt soft margin boosting to overcome overfitting. In the second level, the RVM algorithm is revised for boosting so that different RVM agents can be generated from the updated instance space of the data. In the third level, the perceptron Kernel is employed in RVM to simulate infinite subagents. Our system RVM"I"d"e"a"l also shares some good properties, such as good generalization performance, immunity to overfitting and predicting the distance to default. According to the experimental results, our proposed system can achieve better performance in term of sensitivity, specificity and overall accuracy.

[1]  L. Thomas.  A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers , 2000 .

[2]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[3]  Gunnar Rätsch,et al.  Soft Margins for AdaBoost , 2001, Machine Learning.

[4]  Richard T. Redmond,et al.  Expert systems for bond rating: a comparative analysis of statistical, rule‐based and neural network systems , 1993 .

[5]  Soushan Wu,et al.  Credit rating analysis with support vector machines and neural networks: a market comparative study , 2004, Decis. Support Syst..

[6]  Jen-Ying Shih,et al.  A study of Taiwan's issuer credit rating systems using support vector machines , 2006, Expert Syst. Appl..

[7]  W. Beaver.  Financial Ratios As Predictors Of Failure , 1966 .

[8]  Mu-Chen Chen,et al.  Credit scoring with a data mining approach based on support vector machines , 2007, Expert Syst. Appl..

[9]  Martin Schader,et al.  Between Data Science and Applied Data Analysis , 2003 .

[10]  Arnaud de Servigny,et al.  Measuring and Managing Credit Risk , 2004 .

[11]  Andrew P. Sage,et al.  Uncertainty in Artificial Intelligence , 1987, IEEE Transactions on Systems, Man, and Cybernetics.

[12]  William W. Lang,et al.  Competitive Effects of Basel II on U . S . Bank Credit Card Lending , 2006 .

[13]  Ralf Stecking,et al.  Support Vector Machines for Credit Scoring: Comparing to and Combining With Some Traditional Classification Methods , 2003 .

[14]  Tarun K. Sen,et al.  Predicting Bond Ratings Using Neural Networks: A Comparison with Logistic Regression , 1997, Intell. Syst. Account. Finance Manag..

[15]  Wuyi Yue,et al.  Support vector machine based multiagent ensemble learning for credit risk evaluation , 2010, Expert Syst. Appl..

[16]  Brian Coyle.  Corporate credit analysis , 2000 .

[17]  Ling Li,et al.  Infinite Ensemble Learning with Support Vector Machines , 2005, ECML.

[18]  J. Galindo,et al.  Credit Risk Assessment Using Statistical and Machine Learning: Basic Methodology and Risk Modeling Applications , 2000 .

[19]  Hsuan-Tien Lin,et al.  Novel Distance-Based SVM Kernels for Infinite Ensemble Learning , 2005 .

[20]  George Eastman House,et al.  Sparse Bayesian Learning and the Relevance Vector Machine , 2001 .

[21]  Christopher M. Bishop,et al.  Variational Relevance Vector Machines , 2000, UAI.

[22]  Shu Ling Lin,et al.  A new two-stage hybrid approach of credit risk in banking industry , 2009, Expert Syst. Appl..

[23]  Louis H. Ederington.  CLASSIFICATION MODELS AND BOND RATINGS , 1985 .

[24]  Johan A. K. Suykens,et al.  Benchmarking state-of-the-art classification algorithms for credit scoring , 2003, J. Oper. Res. Soc..

[25]  Yoav Freund,et al.  Experiments with a New Boosting Algorithm , 1996, ICML.

[26]  William W. Lang,et al.  Competitive Effects of Basel II on U.S. Bank Credit Card Lending , 2007 .

[27]  Ling Li,et al.  Support Vector Machinery for Infinite Ensemble Learning , 2008, J. Mach. Learn. Res..

[28]  Kin Keung Lai,et al.  Credit risk assessment with a multistage neural network ensemble learning approach , 2008, Expert Syst. Appl..