A real-valued genetic algorithm to optimize the parameters of support vector machine for predicting bankruptcy

Two parameters, C and σ, must be carefully predetermined in establishing an efficient support vector machine (SVM) model. Therefore, the purpose of this study is to develop a genetic-based SVM (GA-SVM) model that can automatically determine the optimal parameters, C and σ, of SVM with the highest predictive accuracy and generalization ability simultaneously. This paper pioneered on employing a real-valued genetic algorithm (GA) to optimize the parameters of SVM for predicting bankruptcy. Additionally, the proposed GA-SVM model was tested on the prediction of financial crisis in Taiwan to compare the accuracy of the proposed GA-SVM model with that of other models in multivariate statistics (DA, logit, and probit) and artificial intelligence (NN and SVM). Experimental results show that the GA-SVM model performs the best predictive accuracy, implying that integrating the RGA with traditional SVM model is very successful.

[1]  Francis Eng Hock Tay,et al.  Improved financial time series forecasting by combining Support Vector Machines with self-organizing feature map , 2001, Intell. Data Anal..

[2]  Sancho Salcedo-Sanz,et al.  Genetic programming for the prediction of insolvency in non-life insurance companies , 2005, Comput. Oper. Res..

[3]  Robert W. Ingram,et al.  Tests of the generalizability of Altman's bankruptcy prediction model , 2001 .

[4]  Leora F. Klapper,et al.  Resolution of corporate distress in East Asia , 2003 .

[5]  John J. Grefenstette,et al.  Optimization of Control Parameters for Genetic Algorithms , 1986, IEEE Transactions on Systems, Man, and Cybernetics.

[6]  Melody Y. Kiang,et al.  Managerial Applications of Neural Networks: The Case of Bank Failure Predictions , 1992 .

[7]  Simon Haykin,et al.  Support vector machines for dynamic reconstruction of a chaotic system , 1999 .

[8]  F. Girosi,et al.  Nonlinear prediction of chaotic time series using support vector machines , 1997, Neural Networks for Signal Processing VII. Proceedings of the 1997 IEEE Signal Processing Society Workshop.

[9]  Kyoung-jae Kim,et al.  Financial time series forecasting using support vector machines , 2003, Neurocomputing.

[10]  E. Deakin.  Discriminant Analysis Of Predictors Of Business Failure , 1972 .

[11]  Robert Tibshirani,et al.  An Introduction to the Bootstrap , 1994 .

[12]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[13]  Lijuan Cao,et al.  Support vector machines experts for time series forecasting , 2003, Neurocomputing.

[14]  S. Sathiya Keerthi,et al.  Evaluation of simple performance measures for tuning SVM hyperparameters , 2003, Neurocomputing.

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

[16]  Lutgarde M. C. Buydens,et al.  Using support vector machines for time series prediction , 2003 .

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

[18]  Ingoo Han,et al.  Hybrid neural network models for bankruptcy predictions , 1996, Decis. Support Syst..

[19]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[20]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[21]  Ramesh Sharda,et al.  A neural network model for bankruptcy prediction , 1990, 1990 IJCNN International Joint Conference on Neural Networks.

[22]  Edward I. Altman,et al.  FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY , 1968 .

[23]  Francis Eng Hock Tay,et al.  Modified support vector machines in financial time series forecasting , 2002, Neurocomputing.

[24]  David B. Fogel,et al.  An introduction to simulated evolutionary optimization , 1994, IEEE Trans. Neural Networks.

[25]  Marjorie B. Platt,et al.  Predicting corporate financial distress: Reflections on choice-based sample bias , 2002 .

[26]  Martin Casdagli,et al.  Nonlinear prediction of chaotic time series , 1989 .

[27]  Marjorie B. Platt,et al.  Probabilistic Neural Networks in Bankruptcy Prediction , 1999 .

[28]  Young-Chan Lee,et al.  Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters , 2005, Expert Syst. Appl..

[29]  Colin Campbell,et al.  Kernel methods: a survey of current techniques , 2002, Neurocomputing.

[30]  J. Hair.  Multivariate data analysis , 1972 .

[31]  Rolph E. Anderson,et al.  Multivariate data analysis (4th ed.): with readings , 1995 .

[32]  C. Zavgren.  ASSESSING THE VULNERABILITY TO FAILURE OF AMERICAN INDUSTRIAL FIRMS: A LOGISTIC ANALYSIS , 1985 .

[33]  Randy L. Haupt,et al.  Practical Genetic Algorithms , 1998 .

[34]  James A. Ohlson.  FINANCIAL RATIOS AND THE PROBABILISTIC PREDICTION OF BANKRUPTCY , 1980 .

[35]  Adeike A. Adewuya.  New methods in genetic search with real-valued chromosomes , 1996 .

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

[38]  Kar Yan Tam,et al.  Neural network models and the prediction of bank bankruptcy , 1991 .

[39]  Fang-Mei Tseng,et al.  A quadratic interval logit model for forecasting bankruptcy , 2005 .

[40]  Andreas Christmann,et al.  Support vector machines , 2008, Data Mining and Knowledge Discovery Handbook.

[41]  Marc Blum.  FAILING COMPANY DISCRIMINANT-ANALYSIS , 1974 .

[42]  E. Altman,et al.  ZETATM analysis A new model to identify bankruptcy risk of corporations , 1977 .

[43]  Nello Cristianini,et al.  Dynamically Adapting Kernels in Support Vector Machines , 1998, NIPS.

[44]  M. Zmijewski.  METHODOLOGICAL ISSUES RELATED TO THE ESTIMATION OF FINANCIAL DISTRESS PREDICTION MODELS , 1984 .

[45]  Yo-Ping Huang,et al.  Real-valued genetic algorithms for fuzzy grey prediction system , 1997, Fuzzy Sets Syst..

[46]  Marjorie B. Platt,et al.  DEVELOPMENT OF A CLASS OF STABLE PREDICTIVE VARIABLES: THE CASE OF BANKRUPTCY PREDICTION , 1990 .