The use of hybrid manifold learning and support vector machines in the prediction of business failure

The prediction of business failure is an important and challenging issue that has served as the impetus for many academic studies over the past three decades. This paper proposes a hybrid manifold learning approach model which combines both isometric feature mapping (ISOMAP) algorithm and support vector machines (SVM) to predict the failure of firms based on past financial performance data. By making use of the ISOMAP algorithm to perform dimension reduction, is then utilized as a preprocessor to improve business failure prediction capability by SVM. To create a benchmark, we further compare principal component analysis (PCA) and SVM with our proposed hybrid approach. Analytic results demonstrate that our hybrid approach not only has the best classification rate, but also produces the lowest incidence of Type II errors, and is capable of achieving an improved predictive accuracy and of providing guidance for decision makers to detect and prevent potential financial crises in the early stages.

[1]  Chih-Chou Chiu,et al.  Financial time series forecasting using independent component analysis and support vector regression , 2009, Decis. Support Syst..

[2]  Hui Li,et al.  Predicting business failure using multiple case-based reasoning combined with support vector machine , 2009, Expert Syst. Appl..

[3]  A. Elgammal,et al.  Separating style and content on a nonlinear manifold , 2004, CVPR 2004.

[4]  Wei-Yin Loh,et al.  Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..

[5]  Hui Li,et al.  Majority voting combination of multiple case-based reasoning for financial distress prediction , 2009, Expert Syst. Appl..

[6]  M. Turk,et al.  Probabilistic expression analysis on manifolds , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[7]  Carl Gold,et al.  Model selection for support vector machine classification , 2002, Neurocomputing.

[8]  Joshua B. Tenenbaum,et al.  Global Versus Local Methods in Nonlinear Dimensionality Reduction , 2002, NIPS.

[9]  Stan Z. Li,et al.  Nearest manifold approach for face recognition , 2004, Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings..

[10]  Olli Silven,et al.  Comparison of dimensionality reduction methods for wood surface inspection , 2003, International Conference on Quality Control by Artificial Vision.

[11]  Eric O. Postma,et al.  Dimensionality Reduction: A Comparative Review , 2008 .

[12]  Mikhail Belkin,et al.  Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering , 2001, NIPS.

[13]  E. Mine Cinar,et al.  Neural Networks: A New Tool for Predicting Thrift Failures , 1992 .

[14]  Jezekiel Ben-Arie,et al.  Minimal classification method with error-correcting codes for multiclass recognition , 2005, Int. J. Pattern Recognit. Artif. Intell..

[15]  Bernardete Ribeiro,et al.  Supervised Isomap with Dissimilarity Measures in Embedding Learning , 2008, CIARP.

[16]  Antanas Verikas,et al.  Hybrid and ensemble-based soft computing techniques in bankruptcy prediction: a survey , 2010, Soft Comput..

[17]  Michael Y. Hu,et al.  Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis , 1999, Eur. J. Oper. Res..

[18]  Federico Girosi,et al.  Training support vector machines: an application to face detection , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[19]  Kevin G. Coleman,et al.  Neural networks for bankruptcy prediction: the power to solve financial problems , 1991 .

[20]  James H. Scott The probability of bankruptcy: A comparison of empirical predictions and theoretical models , 1981 .

[21]  Chih-Fong Tsai,et al.  Feature selection in bankruptcy prediction , 2009, Knowl. Based Syst..

[22]  Harris Drucker,et al.  Support vector machines for spam categorization , 1999, IEEE Trans. Neural Networks.

[23]  Ramesh Sharda,et al.  Bankruptcy prediction using neural networks , 1994, Decis. Support Syst..

[24]  E. Altman The success of business failure prediction models: An international survey , 1984 .

[25]  Bernhard Schölkopf,et al.  Improving the Accuracy and Speed of Support Vector Machines , 1996, NIPS.

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

[27]  C. Zavgren,et al.  The prediction of corporate failure: The state of the art , 1983 .

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

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

[30]  Maja J. Mataric,et al.  A spatio-temporal extension to Isomap nonlinear dimension reduction , 2004, ICML.

[31]  S T Roweis,et al.  Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.

[32]  Hui Li,et al.  Ranking-order case-based reasoning for financial distress prediction , 2008, Knowl. Based Syst..

[33]  Matti Pietikäinen,et al.  Unsupervised learning using locally linear embedding: experiments with face pose analysis , 2002, Object recognition supported by user interaction for service robots.

[34]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[35]  Byeong Seok Ahn,et al.  The integrated methodology of rough set theory and artificial neural network for business failure prediction , 2000 .

[36]  Steven Salzberg,et al.  On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach , 1997, Data Mining and Knowledge Discovery.

[37]  D. Stoneking Improving the manufacturability of electronic designs , 1999 .

[38]  Richard A. Johnson,et al.  Applied Multivariate Statistical Analysis , 1983 .

[39]  F Jones,et al.  CURRENT TECHNIQUES IN BANKRUPTCY PREDICTION , 1987 .

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

[41]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques with Java implementations , 2002, SGMD.

[42]  Bart Baesens,et al.  Predicting going concern opinion with data mining , 2008, Decis. Support Syst..

[43]  Thorsten Joachims,et al.  Learning to classify text using support vector machines - methods, theory and algorithms , 2002, The Kluwer international series in engineering and computer science.

[44]  Stan Z. Li,et al.  Nonlinear mapping from multi-view face patterns to a Gaussian distribution in a low dimensional space , 2001, Proceedings IEEE ICCV Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems.

[45]  K. Keasey,et al.  Financial Distress Prediction Models: A Review of Their Usefulness1 , 1991 .

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

[47]  Mikhail F. Kanevski,et al.  Learning Manifolds in Forensic Data , 2006, ICANN.

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

[49]  Carl-Fredrik Westin,et al.  Coloring of DT-MRI Fiber Traces Using Laplacian Eigenmaps , 2003, EUROCAST.

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

[51]  Stephanie M. Bryant,et al.  A case-based reasoning approach to bankruptcy prediction modeling , 1996 .

[52]  Qingzhong Liu,et al.  Learning Manifolds for Bankruptcy Analysis , 2008, ICONIP.

[53]  Yongsheng Ding,et al.  Forecasting financial condition of Chinese listed companies based on support vector machine , 2008, Expert Syst. Appl..

[54]  Vadlamani Ravi,et al.  Bankruptcy prediction in banks and firms via statistical and intelligent techniques - A review , 2007, Eur. J. Oper. Res..

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

[56]  Shai Ben-David,et al.  Learning Distributions by Their Density Levels: A Paradigm for Learning without a Teacher , 1997, J. Comput. Syst. Sci..

[57]  Michael Brady,et al.  Novelty detection for the identification of masses in mammograms , 1995 .

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

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

[60]  Ming-Hsuan Yang,et al.  Face recognition using extended isomap , 2002, Proceedings. International Conference on Image Processing.

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

[62]  Constantin Zopounidis,et al.  A survey of business failures with an emphasis on prediction methods and industrial applications , 1996 .

[63]  Efraim Turban,et al.  Neural Networks in Finance and Investing: Using Artificial Intelligence to Improve Real-World Performance , 1992 .

[64]  J. Tenenbaum,et al.  A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.

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

[66]  F. Tay,et al.  Application of support vector machines in financial time series forecasting , 2001 .

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

[68]  A. Elgammal,et al.  Inferring 3D body pose from silhouettes using activity manifold learning , 2004, CVPR 2004.

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

[70]  Rani Siromoney,et al.  Puzzle Grammars and Context-Free Array Grammars , 1991, Int. J. Pattern Recognit. Artif. Intell..