An improved boosting based on feature selection for corporate bankruptcy prediction

With the recent financial crisis and European debt crisis, corporate bankruptcy prediction has become an increasingly important issue for financial institutions. Many statistical and intelligent methods have been proposed, however, there is no overall best method has been used in predicting corporate bankruptcy. Recent studies suggest ensemble learning methods may have potential applicability in corporate bankruptcy prediction. In this paper, a new and improved Boosting, FS-Boosting, is proposed to predict corporate bankruptcy. Through injecting feature selection strategy into Boosting, FS-Booting can get better performance as base learners in FS-Boosting could get more accuracy and diversity. For the testing and illustration purposes, two real world bankruptcy datasets were selected to demonstrate the effectiveness and feasibility of FS-Boosting. Experimental results reveal that FS-Boosting could be used as an alternative method for the corporate bankruptcy prediction.

[1]  Diego Andina,et al.  Multiple proportion case-basing driven CBRE and its application in the evaluation of possible failure of firms , 2013, Int. J. Syst. Sci..

[2]  R. Polikar,et al.  Ensemble based systems in decision making , 2006, IEEE Circuits and Systems Magazine.

[3]  S. Raghavan,et al.  Diversification for better classification trees , 2006, Comput. Oper. Res..

[4]  Hiroshi Motoda,et al.  Feature Selection for Knowledge Discovery and Data Mining , 1998, The Springer International Series in Engineering and Computer Science.

[5]  Huan Liu,et al.  Feature Selection for Classification , 1997, Intell. Data Anal..

[6]  Loris Nanni,et al.  An experimental comparison of ensemble of classifiers for bankruptcy prediction and credit scoring , 2009, Expert Syst. Appl..

[7]  R. C. West.  A factor-analytic approach to bank condition , 1985 .

[8]  Michael D. Vose,et al.  No Free Lunch and Benchmarks , 2013, Evolutionary Computation.

[9]  Juan Julián Merelo Guervós,et al.  Comparing multiobjective evolutionary ensembles for minimizing type I and II errors for bankruptcy prediction , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[10]  Michael J. Shaw,et al.  Inductive learning for risk classification , 1990, IEEE Expert.

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

[12]  Ian Witten,et al.  Data Mining , 2000 .

[13]  Hui Li,et al.  Gaussian case-based reasoning for business failure prediction with empirical data in China , 2009, Inf. Sci..

[14]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[15]  Francisco Javier de Cos Juez,et al.  A hybrid device for the solution of sampling bias problems in the forecasting of firms' bankruptcy , 2012, Expert Syst. Appl..

[16]  W. Pietruszkiewicz,et al.  Dynamical systems and nonlinear Kalman filtering applied in classification , 2008, 2008 7th IEEE International Conference on Cybernetic Intelligent Systems.

[17]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[18]  Kyung-shik Shin,et al.  A genetic algorithm application in bankruptcy prediction modeling , 2002, Expert Syst. Appl..

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

[20]  David L. Olson,et al.  Comparative analysis of data mining methods for bankruptcy prediction , 2012, Decis. Support Syst..

[21]  Jian Ma,et al.  A comparative assessment of ensemble learning for credit scoring , 2011, Expert Syst. Appl..

[22]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[23]  Pat Langley,et al.  Selection of Relevant Features and Examples in Machine Learning , 1997, Artif. Intell..

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

[25]  D. Opitz,et al.  Popular Ensemble Methods: An Empirical Study , 1999, J. Artif. Intell. Res..

[26]  Hui Li,et al.  Financial distress prediction using support vector machines: Ensemble vs. individual , 2012, Appl. Soft Comput..

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

[28]  Li-Chiu Chi,et al.  Neural networks analysis in business failure prediction of Chinese importers: A between-countries approach , 2005, Expert Syst. Appl..

[29]  B.V. Dasarathy,et al.  A composite classifier system design: Concepts and methodology , 1979, Proceedings of the IEEE.

[30]  George Forman,et al.  An Extensive Empirical Study of Feature Selection Metrics for Text Classification , 2003, J. Mach. Learn. Res..

[31]  R. Schapire.  The Strength of Weak Learnability , 1990, Machine Learning.

[32]  Lars Kai Hansen,et al.  Neural Network Ensembles , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[33]  Juan José Rodríguez Diez,et al.  Rotation Forest: A New Classifier Ensemble Method , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[34]  Ron Kohavi,et al.  Wrappers for Feature Subset Selection , 1997, Artif. Intell..

[35]  David A. Elizondo,et al.  Bankruptcy forecasting: An empirical comparison of AdaBoost and neural networks , 2008, Decis. Support Syst..

[36]  Tin Kam Ho,et al.  The Random Subspace Method for Constructing Decision Forests , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[37]  Anil K. Jain,et al.  Feature Selection: Evaluation, Application, and Small Sample Performance , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[38]  Kin Keung Lai,et al.  Bankruptcy prediction using SVM models with a new approach to combine features selection and parameter optimisation , 2014, Int. J. Syst. Sci..

[39]  Chih-Fong Tsai,et al.  Using neural network ensembles for bankruptcy prediction and credit scoring , 2008, Expert Syst. Appl..

[40]  Ingoo Han,et al.  A case-based approach using inductive indexing for corporate bond rating , 2001, Decis. Support Syst..

[41]  Prabhas Chongstitvatana,et al.  Feature Selection by Weighted-SNR for Cancer Microarray Data Classification , 2008 .

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

[43]  Jian Ma,et al.  Two credit scoring models based on dual strategy ensemble trees , 2012, Knowl. Based Syst..

[44]  Sotiris B. Kotsiantis,et al.  Forecasting Corporate Bankruptcy with an Ensemble of Classifiers , 2012, SETN.

[45]  Thomas G. Dietterich.  Machine-Learning Research , 1997, AI Mag..

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

[47]  Johan A. K. Suykens,et al.  Bankruptcy prediction with least squares support vector machine classifiers , 2003, 2003 IEEE International Conference on Computational Intelligence for Financial Engineering, 2003. Proceedings..