Feature selection to diagnose a business crisis by using a real GA-based support vector machine: An empirical study

This research is aimed at establishing the diagnosis models for business crises through integrating a real-valued genetic algorithm to determine the optimum parameters and SVM to perform learning and classification on data. After finishing the training processes, the proposed GA-SVM can reach a prediction accuracy of up to 95.56% for all the tested business data. Particularly, only six influential features are included in the proposed model with intellectual capital and financial features after the 2-phase selecting process; the six features are ordinary and widely available from public business reports. The proposed GA-SVM is available for business managers to conduct self-diagnosis in order to realize whether business units are really facing a crisis.

[1]  Chih-Jen Lin,et al.  A Practical Guide to Support Vector Classication , 2008 .

[2]  Kyung-shik Shin,et al.  An application of support vector machines in bankruptcy prediction model , 2005, Expert Syst. Appl..

[3]  Guido Dedene,et al.  A Comparison of State-of-The-Art Classification Techniques for Expert Automobile Insurance Claim Fraud Detection , 2002 .

[4]  Chih-Jen Lin,et al.  The analysis of decomposition methods for support vector machines , 2000, IEEE Trans. Neural Networks Learn. Syst..

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

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

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

[8]  S. Mann.  Intellectual Capital: The New Wealth of Organizations , 1999 .

[9]  Ingoo Han,et al.  Bankruptcy prediction using case-based reasoning, neural networks, and discriminant analysis , 1997 .

[10]  Vasilis Masoulas,et al.  Organisational requirements definition for intellectual capital management , 1998 .

[11]  Kuan-Yu Chen,et al.  A hybrid SARIMA and support vector machines in forecasting the production values of the machinery industry in Taiwan , 2007, Expert Syst. Appl..

[12]  Ingoo Han,et al.  Hybrid genetic algorithms and support vector machines for bankruptcy prediction , 2006, Expert Syst. Appl..

[13]  Fernando Pérez-Cruz,et al.  Feature Selection via Genetic Optimization , 2002, ICANN.

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

[15]  Wu Meng,et al.  Application of Support Vector Machines in Financial Time Series Forecasting , 2007 .

[16]  N. Bontis.  Intellectual capital: an exploratory study that develops measures and models , 1998 .

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

[18]  V. Srinivasan,et al.  CGX: An expert support system for credit granting , 1990 .

[19]  Paul Gray,et al.  Special Section: Data Mining , 1999, J. Manag. Inf. Syst..

[20]  J. Efrim Boritz,et al.  Effectiveness of neural network types for prediction of business failure , 1995 .

[21]  E. Altman.  Corporate financial distress : a complete guide to predicting, avoiding, and dealing with bankruptcy , 1983 .

[22]  Sharon A. DeVaney,et al.  Predicting Business Failure of Retail Firms: An Analysis Using Mixed Industry Models , 1998 .

[23]  H. Frydman,et al.  Introducing Recursive Partitioning for Financial Classification: The Case of Financial Distress , 1985 .

[24]  Kezhi Mao,et al.  Feature subset selection for support vector machines through discriminative function pruning analysis , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[25]  D. Bertrand,et al.  Feature selection by a genetic algorithm. Application to seed discrimination by artificial vision , 1998 .

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

[27]  Kalyanmoy Deb,et al.  Simulated Binary Crossover for Continuous Search Space , 1995, Complex Syst..

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

[29]  Charles E. Heckler,et al.  Applied Multivariate Statistical Analysis , 2005, Technometrics.

[30]  Stuart C. Gilson.  Management turnover and financial distress , 1989 .

[31]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[32]  Edward I. Altman,et al.  Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience) , 1994 .

[33]  Johan A. K. Suykens,et al.  Financial time series prediction using least squares support vector machines within the evidence framework , 2001, IEEE Trans. Neural Networks.

[34]  Yiming Yang,et al.  A re-examination of text categorization methods , 1999, SIGIR '99.

[35]  Cheng-Lung Huang,et al.  A GA-based feature selection and parameters optimizationfor support vector machines , 2006, Expert Syst. Appl..

[36]  Chang Wook Ahn,et al.  On the practical genetic algorithms , 2005, GECCO '05.

[37]  Marimuthu Palaniswami,et al.  Selecting bankruptcy predictors using a support vector machine approach , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

[38]  Kalyanmoy Deb,et al.  A combined genetic adaptive search (GeneAS) for engineering design , 1996 .

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

[40]  S. Sathiya Keerthi,et al.  Convergence of a Generalized SMO Algorithm for SVM Classifier Design , 2002, Machine Learning.

[41]  P. Falbo,et al.  Credit-scoring by enlarged discriminant models , 1991 .

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

[43]  M. S. Sánchez,et al.  Efficiency of multi-layered feed-forward neural networks on classification in relation to linear discriminant analysis, quadratic discriminant analysis and regularized discriminant analysis , 1995 .

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

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

[46]  Rashmi Malhotra,et al.  Differentiating between Good Credits and Bad Credits Using Neuro-Fuzzy Systems , 2001, Eur. J. Oper. Res..

[47]  Bernhard Schölkopf,et al.  Feature selection for support vector machines by means of genetic algorithm , 2003, Proceedings. 15th IEEE International Conference on Tools with Artificial Intelligence.

[48]  Pamela K. Coats,et al.  Recognizing Financial Distress Patterns Using a Neural Network Tool , 1993 .

[49]  Daniel Martin,et al.  Early warning of bank failure: A logit regression approach , 1977 .

[50]  Thorsten Joachims,et al.  Text Categorization with Support Vector Machines: Learning with Many Relevant Features , 1998, ECML.