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

Knowledge-Based Systems

Volumn 24, Issue 1, 2011, Pages 95-101

  Lin, Fengyi ^a   Yeh, Ching Chiang ^b   Lee, Meng Yuan ^a  

^a NATIONAL TAIPEI UNIVERSITY OF TECHNOLOGY   (Taiwan)

^b NATIONAL TAIPEI UNIVERSITY OF BUSINESS   (Taiwan)

Author keywords

Business failure; ISOMAP; Manifold learning; Support vector machine

Indexed keywords

BUSINESS FAILURE; BUSINESS FAILURE PREDICTION; CLASSIFICATION RATES; DECISION MAKERS; DIMENSION REDUCTION; FINANCIAL CRISIS; FINANCIAL PERFORMANCE; HYBRID APPROACH; ISOMAP; ISOMAP ALGORITHMS; ISOMETRIC FEATURE MAPPING; MANIFOLD LEARNING; PREDICTIVE ACCURACY; PREPROCESSORS; TYPE II ERROR;

FORECASTING; GEARS; PLANT SHUTDOWNS; PRINCIPAL COMPONENT ANALYSIS; VECTORS;

SUPPORT VECTOR MACHINES;

EID: 77958001182     PISSN: 09507051     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.knosys.2010.07.009     Document Type: Article

References (71)

1. B.S. Ahn, S.S. Cho, and C.Y. Kim The integrated methodology of rough set theory and artificial neural network for business failure prediction Expert Systems with Applications 18 2000 65 74
2. E.I. Altman Financial ratios discriminant analysis and the prediction of corporate bankruptcy Journal of Finance 23 4 1968 589 609
3. E.I. Altman The success of business failure prediction models: an international survey Journal of Banking and Finance 8 2 1984 171 198
4. E.I. Altman, R.G. Haldeman, and P. Narayanan Zeta analysis Journal of Banking and Finance June 1977 29 51
5. W.H. Beaver Financial ratios as predictors of failure empirical research in accounting: selected studies Supplement to the Journal of Accounting Research 4 1966 179 199
6. M. Belkin, and P. Niyogi Laplacian eigenmaps and spectral techniques for embedding and clustering Advances in Neural Information Processing Systems 14 2002 585 591
7. S. Ben-David, and M. Lindenbaum Learning distributions by their density levels: a paradigm for learning without a teacher Journal of Computer and System Sciences 55 1997 171 182
8. M. Blum Failure company discriminant analysis Journal of Accounting Research 1974 1 25
9. L. Breiman, J.H. Friedman, R.A. Olshen, and C.J. Stone Classification and Regression Trees 1984 Wadsworth Pacific Grove, CA
10. A. Brun, H.J. Park, H. Knutsson, C.F. Westin, Coloring of DT-MRI fiber traces using Laplacian eigenmaps, in: Proc. Ninth International Conference on Computer Aided Systems Theory, vol. 2809, 2003.
11. S.M. Bryant A case-based reasoning approach to bankruptcy prediction modeling Intelligent System Accounting, Financial and Management 6 1997 195 214
12. C.J.C. Burges, and B. Scholkopf Improving the accuracy and speed of support vector machines M. Mozer, M. Jordan, T. Petsche, Advances in Neural Information Processing Systems 1997 MIT Press Cambridge, MA 475 481
13. C.C. Chang, C.J. Lin, LIBSVM: A Library for Support Vector Machines, 2001, < http://www.csie.ntu.edu.tw/~cjlin/libsvm >.
14. Y. Chang, C. Hu, M. Matthew Turk, Probabilistic expression analysis on manifolds, in: Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, 2004, pp. 520-527.
15. K.G. Coleman, T.J. Graettinger, and W.F. Lawrence Neural networks for bankruptcy prediction: the power to solve financial problems AI Review 1991 48 50
16. C. Cortes, and V.N. Vapnik Support vector networks Machine Learning 20 1995 273 297
17. E.B. Deakin A discriminant analysis of predictors of business failure Journal of Accounting Research 10 1972 167 179
18. V. De Silva, and J.B. Tenenbaum Global Versus Local Methods in Nonlinear Dimensionality Reduction 2003 MIT Press pp. 705-712
19. A.I. Dimitras, S.H. Zanakis, and C. Zopounidis A survey of business failures with an emphasis on prediction methods and industrial applications European Journal of Operational Research 90 1996 487 513
20. Y.S. Ding, X.P. Song, and Y.M. Zen Forecasting financial condition of Chinese listed companies based on support vector machine Expert Systems with Applications 34 2008 3081 3089
21. H. Drucker, D. Wu, and V.N. Vapnik Support vector machines for spam categorization IEEE Transactions Neural Networks 10 5 1999 1048 1054
22. A. Elgammal, C.S. Lee, Separating style and content on a nonlinear manifold, in: Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004, pp. 478-489.
23. A. Elgammal, C.S. Lee, Inferring 3D body pose from silhouettes using activity manifold learning, in: Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, 2004, pp. 681-688.
24. C. Gold, and P. Sollish Model selection for support vector machine classification Neurcomputing 55 2005 221 249
25. A. Hadid, O. Kouropteva, M. Pietikainen, Unsupervised learning using locally linear embedding: experiments in face pose analysis, in: Proc. 16th International Conference on Pattern Recognition, vol. I, 2002, pp. 111-114.
26. Z. Huang, H. Chen, C.J. Hsu, W.H. Chen, and S. Wu Credit rating analysis with support vector machine and neural networks: a market comparative study Decision Support Systems 37 2004 543 558
27. O. Jenkins, M. Mataric, A spatio-temporal extension to Isomap nonlinear dimension reduction, in: Proc. 21st International Conference on Machine Learning, 2004.
28. T. Joachims Learning to Classify Text Using Support Vector Machines 2002 Kluwer Academic Publishers London
29. R.A. Johnson, and D.W. Wichern Applied Multivariate Statistical Analysis fifth ed. 2002 Prentice-Hall Upper Saddle River, NJ
30. F.L. Jones Current techniques in bankruptcy prediction Journal of Accounting Literature 6 1987 131 164
31. K. Keasey, and R. Watson Financial distress prediction models: a review of their usefulness British Journal of Management 2 1991 89 102
32. K.J. Kim Financial time series forecasting using support vector machines Neurocomputing 55 1/2 2003 307 319
33. P.R. Kumar, and V. Ravi Bankruptcy prediction in banks and firms via statistical and intelligent techniques - a review European Journal of Operational Research 180 1 2007 1 28
34. H. Li, and J. Sun Ranking-order case-based reasoning for financial distress prediction Knowledge-based Systems 21 2008 868 878
35. H. Li, and J. Sun Predicting financial distress using multiple case-based reasoning combine with support vector machine Expert Systems with Applications 2009
36. S.Z. Li, R.Z. Xiao, Y. Li, H.J. Zhang, Nonlinear mapping from multi-view face patterns to a gaussian distribution in a low dimensional space, in: Proc. IEEE ICCV Workshop on Recognition, Analysis and Tracking of Faces and Gestures in Real-time Systems, 2001.
37. C.J. Lu, T.S. Lee, and C.C. Chiu Financial time series forecasting using independent component analysis and support vector regression Decision Support Systems 2009
38. L.J.P. Van der Maaten, E.O. Postma, H.J. van den Herik, Dimensionality reduction: a comparative review, 2007, < http://www.cs.unimaas.nl/I. vandermaaten/dr/DR-draft.pdf >.
39. D. Martens, L. Bruynseels, B. Baesens, M. Willekens, and J. Vanthienen Predicting going concern opinion with data mining Decision Support Systems 45 2008 765 777
40. J.H. Min, and Y.C. Lee Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters Expert Systems with Applications 28 2005 603 614
41. S. Mukherjee, E. Osuna, F. Girosi, Nonlinear prediction of chaotic time series using support vector, in: Proc. IEEE Workshop on Neural Networks for Signal Processing, Amelia Island, FL, 1997, pp. 511-520.
42. M. Niskanen, O. Silvn, Comparison of dimensionality reduction methods for wood surface inspection, in: Proc. Sixth International Conference on Quality Control by Artificial Vision, 2003, pp. 178-188.
43. J.A. Ohlson Financial ratios and the probabilistic prediction of bankruptcy Journal of Accounting Research 1980 109 131
44. E. Osuna, R. Freund, F. Girosi, Training support vector machines: an application to face detection, in: Proc. Computer Vision and Pattern Recognition, 1997, pp. 130-136.
45. E. Rahimian, S. Singh, T. Thammachote, and R. Virmani Bankruptcy prediction by neural networks E. Trippi, E. Turban, Neural Networks in Finance and Investing: Using Artificial Intelligence to Improve Real-World Performance 1993 Probus Publishing Chicago 159 176
46. F. Ratle, A.L. Terrettaz, M. Kanevski, P. Esseiva, and O. Ribaux S. Kollias, Learning Manifolds in Forensic Data 2006 Springer-Verlag Berlin, Heidelberg 894 903
47. B. Ribeiro, C. Silva, A. Sung, A.Vieira, J. Neves, J. Duarte, Q. Liu, Learning manifolds for bankruptcy analysis, in: M. Koppen et al. (Eds.), Advances in Neuro-information Processing, Lecture Notes in Computer Science vol. 5506:1, 2009, pp. 722-729.
48. B. Ribeiro, A. Vieira, and J. Neves Supervised isomap with dissimilarity measures in embedding learning Proc. Ibero-American Conference on Pattern Recognition, Progress in Pattern Recognition, Image Analysis and Applications Lecture Notes in Computer Science vol. 5197 2008 Springer Berlin, Heidelberg 389 396
49. S.T. Roweis, and L.K. Saul Nonlinear dimensionality reduction by locally linear embedding Science 290 5500 2000 2323 2326
50. L.M. Salchengerger, E.M. Cinar, and N.A. Lash Neural networks: a new tool for prediction thrift failures Decision Sciences 23 1992 899 916
51. S.L. Salzberg On comparing classifiers: pitfalls to avoid and a recommended approach Data Mining and Knowledge Discovery 1 1997 317 327
52. J. Scott The probability of bankruptcy: a comparison of empirical predictions and theoretical models Journal of Banking and Finance 5 1981 317 344
53. R. Sharda, and R.L. Wilson Neural networks experiments in business-failure forecasting: predictive performance measurement issues International Journal of Computational Intelligence and Organizations 1 2 1996 107 117
54. K.S. Shin, T.S. Lee, and H.J. Kim An application of support vector machines in bankruptcy prediction model Expert Systems with Applications 28 2005 127 135
55. D.M. Sinalingam, and N. Pandia Minimal classification method with error correlation codes for multiclass recognition International Journal of Pattern Recognition and Artificial Intelligence 5 2005 663 680
56. D. Stoneking Improving the manufacturability of electronic designs IEEE Spectrum 36 6 1999 70 76
57. K.Y. Tam, and M. Kiang Managerial applications of neural networks: the case of bank failure predictions Management Science 38 7 1992 926 947
58. L. Tarassenko, P. Hayton, N. Cerneaz, M. Brady, Novelty detection for the identification of masses in mammograms, in Proc. Fourth IEE International Conference on Artificial Neural Networks, Cambridge, 1995, pp. 442-447.
59. F.E.H. Tay, and L. Cao Application of support vector machines in financial time series forecasting Omega 29 2001 309 317
60. J.B. Tenenbaum, V. de Silva, and J.C. Langford A global geometric framework for nonlinear dimensionality reduction Science 290 2000 2319 2323
61. C.F. Tsai Feature selection in bankruptcy prediction Knowledge-based Systems 22 2 2009 120 127
62. V.N. Vapnik The Nature of Statistical Learning Theory second ed. 1995 Springer-Verlag New York
63. V.N. Vapnik Statistical Learning Theory 1998 Springer New York
64. A. Verikas, Z. Kalsyte, M. Bacauskiene, A. Gelzinis, Hybrid and ensemble-based soft computing techniques in bankruptcy prediction: a survey. Soft Computing - A Fusion of Foundations, Methodologies and Applications, September 2009 (online).
65. R.L. Wilson, and R. Sharda Bankruptcy prediction using neural networks Decision Support Systems 11 1994 545 557
66. I.H. Witten, and E. Frank Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations 2000 Morgan Kaufman San Francisco, CA
67. M.H. Yang, Face recognition using extended Isomap, in: IEEE International Conference on Image Processing, vol. II, 2002, pp. 117-120.
68. C.V. Zavgren The prediction of corporate failure: the state of the art Journal of Financial Literature 2 1983 1 37
69. G. Zhang, M.Y. Hu, B.E. Patuwo, and D.C. Indro Artificial neural networks in bankruptcy predictions: general framework and cross-validation analysis European Journal of Operational Research 116 1999 16 32
70. J. Zhang, S.Z. Li, J. Wang, Nearest manifold approach for face recognition, in: Proc. Sixth International Conference on Automatic Face and Gesture Recognition, Seoul, Korea, 2004.
71. Y.S. Zhu, and Y.Y. Zhang The study on some problems of support vector classifier Computer Engineering and Applications 13 2003 38 66