Evolution strategy based adaptive Lq penalty support vector machines with Gauss kernel for credit risk analysis

Credit risk analysis has long attracted great attention from both academic researchers and practitioners. However, the recent global financial crisis has made the issue even more important because of the need for further enhancement of accuracy of classification of borrowers. In this study an evolution strategy (ES) based adaptive L"q SVM model with Gauss kernel (ES-AL"qG-SVM) is proposed for credit risk analysis. Support vector machine (SVM) is a classification method that has been extensively studied in recent years. Many improved SVM models have been proposed, with non-adaptive and pre-determined penalties. However, different credit data sets have different structures that are suitable for different penalty forms in real life. Moreover, the traditional parameter search methods, such as the grid search method, are time consuming. The proposed ES-based adaptive L"q SVM model with Gauss kernel (ES-AL"qG-SVM) aims to solve these problems. The non-adaptive penalty is extended to (0, 2] to fit different credit data structures, with the Gauss kernel, to improve classification accuracy. For verification purpose, two UCI credit datasets and a real-life credit dataset are used to test our model. The experiment results show that the proposed approach performs better than See5, DT, MCCQP, SVM light and other popular algorithms listed in this study, and the computing speed is greatly improved, compared with the grid search method.

[1]  Joachim Diederich,et al.  Ex-ray: Data mining and mental health , 2007, Appl. Soft Comput..

[2]  Christian Igel,et al.  Evolutionary Optimization of Sequence Kernels for Detection of Bacterial Gene Starts , 2006, ICANN.

[3]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.

[4]  Elpiniki I. Papageorgiou,et al.  A new hybrid method using evolutionary algorithms to train Fuzzy Cognitive Maps , 2005, Appl. Soft Comput..

[5]  Arijit Laha.  Building contextual classifiers by integrating fuzzy rule based classification technique and k-nn method for credit scoring , 2007, Adv. Eng. Informatics.

[6]  Jianping Li,et al.  A weighted Lq adaptive least squares support vector machine classifiers - Robust and sparse approximation , 2011, Expert Syst. Appl..

[7]  Yong Shi,et al.  Classifications of Credit Cardholder Behavior by Using Multiple Criteria Non-linear Programming , 2004, CASDMKM.

[8]  Zhengxin Chen,et al.  A Multi-criteria Convex Quadratic Programming model for credit data analysis , 2008, Decis. Support Syst..

[9]  Jianping Li,et al.  Multiple-kernel SVM based multiple-task oriented data mining system for gene expression data analysis , 2011, Expert Syst. Appl..

[10]  Andrea Roli,et al.  A neural network approach for credit risk evaluation , 2008 .

[11]  Lin Ma,et al.  Mining the customer credit using hybrid support vector machine technique , 2009, Expert Syst. Appl..

[12]  Jianping Li,et al.  A multiple kernel support vector machine scheme for feature selection and rule extraction from gene expression data of cancer tissue , 2007, Artif. Intell. Medicine.

[13]  Satarupa Banerjee,et al.  Text classification: A least square support vector machine approach , 2007, Appl. Soft Comput..

[14]  Jie Yang,et al.  Optimizing the hyper-parameters for SVM by combining evolution strategies with a grid search , 2006 .

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

[16]  Yong Shi,et al.  Classifications Of Credit Cardholder Behavior By Using Fuzzy Linear Programming , 2004, Int. J. Inf. Technol. Decis. Mak..

[17]  S. Gunn.  Support Vector Machines for Classification and Regression , 1998 .

[18]  Jianping Li,et al.  An evolution strategy-based multiple kernels multi-criteria programming approach: The case of credit decision making , 2011, Decis. Support Syst..

[19]  Manolis Papadrakakis,et al.  Soft computing methodologies for structural optimization , 2003, Appl. Soft Comput..

[20]  Masakazu Muramatsu,et al.  An Efficient Support Vector Machine Learning Method with Second-Order Cone Programming for Large-Scale Problems , 2005, Applied Intelligence.

[21]  Jianping Li,et al.  Support Vector Machines Approach to Credit Assessment , 2004, International Conference on Computational Science.

[22]  Wynne Hsu,et al.  Integrating Classification and Association Rule Mining , 1998, KDD.

[23]  Ingo Rechenberg,et al.  Evolutionsstrategie : Optimierung technischer Systeme nach Prinzipien der biologischen Evolution , 1973 .

[24]  Yufeng Liu,et al.  Support vector machines with adaptive Lq penalty , 2007, Comput. Stat. Data Anal..

[25]  Hans-Paul Schwefel,et al.  Evolution strategies – A comprehensive introduction , 2002, Natural Computing.

[26]  JIANPING LI,et al.  Feature Selection via Least Squares Support Feature Machine , 2007, Int. J. Inf. Technol. Decis. Mak..

[27]  Bernhard Schölkopf,et al.  Feature Selection for Support Vector Machines Using Genetic Algorithms , 2004, Int. J. Artif. Intell. Tools.

[28]  Madan Gopal,et al.  Least squares twin support vector machines for pattern classification , 2009, Expert Syst. Appl..

[29]  Jianping Li,et al.  Evolution strategies based adaptive Lp LS-SVM , 2011, Inf. Sci..

[30]  Hussein A. Abdou,et al.  Neural nets versus conventional techniques in credit scoring in Egyptian banking , 2008, Expert Syst. Appl..

[31]  O. Mangasarian,et al.  Massive data discrimination via linear support vector machines , 2000 .

[32]  Wenjian Wang,et al.  Determination of the spread parameter in the Gaussian kernel for classification and regression , 2003, Neurocomputing.

[33]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[34]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[35]  Gunnar Rätsch,et al.  An introduction to kernel-based learning algorithms , 2001, IEEE Trans. Neural Networks.

[36]  Weixuan Xu,et al.  Sparse and robust least squares support vector machine: A linear programming formulation , 2007, 2007 IEEE International Conference on Grey Systems and Intelligent Services.

[37]  W. Vent,et al.  Rechenberg, Ingo, Evolutionsstrategie — Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. 170 S. mit 36 Abb. Frommann‐Holzboog‐Verlag. Stuttgart 1973. Broschiert , 1975 .

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