Adaboost and bagging ensemble approaches with neural network as base learner for financial distress prediction of Chinese construction and Real Estate Companies

Recent Patents on Computer Science

Volumn 6, Issue 1, 2013, Pages 47-59

  Sun, Jie ^a,b   Liao, Bangxiong ^a   Li, Hui ^a  

^a ZHEJIANG NORMAL UNIVERSITY   (China)

^b Dongguk University   (South Korea)

Author keywords

AdaBoost; Bagging; Classifier ensemble; Construction and real estate companies; Financial distress prediction; Neural network

Indexed keywords

BACKPROPAGATION; ECONOMICS; ELECTRONIC TRADING; FINANCIAL MARKETS; FORECASTING; INVESTMENTS; NEURAL NETWORKS;

BACKPROPAGATION NEURAL NETWORKS; BAGGING; CLASSIFIER ENSEMBLES; FINANCIAL DISTRESS PREDICTION; INVESTMENT AND FINANCINGS; REAL ESTATE COMPANIES; SHANGHAI STOCK EXCHANGES; SHENZHEN STOCK EXCHANGES;

ADAPTIVE BOOSTING;

EID: 84876711925     PISSN: 18744796     EISSN: None     Source Type: Journal    
DOI: 10.2174/2213275911306010007     Document Type: Article

References (45)

1. X. Zhang, "Social risks for international players in the construction market: A China study", Habitat International, Vol. 35, no. 3, pp. 514-519, 2011.
2. P.J. Fitzpartrick, "A comparison of ratios of successful industrial enterprises with those of failed Firms", Certified Public Account-ant, Vol. 10, pp. 598-605, Vol. 11, pp. 656-662, Vol. 12, pp. 727-731, 1932.
3. W. Beaver, "Financial ratios as predictors of failure", J. Account. Res., Vol. 4, pp. 71-111, 1966.
4. E.I. Altman, "Financial ratios discriminant analysis and the predic-tion of corporate bankruptcy", J. Finance, Vol. 23(4), pp. 589-609, 1968.
5. J.A. Ohlson, "Financial ratios and probabilistic prediction of bank-ruptcy", Journal of Accounting Research, Vol. 18, no. 1, pp. 109-131, 1980.
6. K.Y. Tam, "Neural network models and the prediction of bank bankruptcy", Omega, Vol. 19(5), pp. 429-445, 1991.
7. H. Jo, and I. Han, "Integration of case-based forecasting, neural network, and discriminant analysis for bankruptcy prediction", Exp. Syst. Applicat., Vol. 11, no. 4, pp. 415-422, 1996.
8. H. Jo and I. Han, "Bankruptcy prediction using case-based reason-ing, neural networks, and discriminant analysis", Exp. Syst. Appli-cat. Vol. 13(2), pp. 97-108, 1997.
9. F. Varetto, "Genetic algorithms applications in the analysis of insolvency risk", J. Banking & Finance, Vol. 22, no. 10-11, pp. 1421-1439, 1998.
10. A.I. Dimitras, R. Slowinski and R. Susmaga, "Business failure prediction using rough sets", Euro. J. Operat. Res., Vol. 114, no. 2, pp. 263-280, 1999.
11. T. E. McKee, "Developing a bankruptcy prediction model by rough sets theory", Int. J. Intelligent Syst. Account., Finance & Manag., Vol. 9(3), pp. 159-173, 2000.
12. K.-S. Shin and Y.-J. Lee, "A genetic algorithm application in bank-ruptcy prediction modeling", Exp. Syst. Applicat., Vol. 23, no. 3, pp. 321-328, 2002.
13. C. S. Park, and I. Han, "A case-based reasoning with the feature weights derived by hierarchy analytic process for bankruptcy pre-diction", Exp. Syst. Applicat., Vol. 23, no. 3, pp. 255-264, 2002.
14. K.-S. Shin, T. S. Lee and H.-J. Kim, "An application of support vector machines in bankruptcy prediction model", Exp. Syst. Appli-cat, Vol. 28, no. 1, pp. 127-135, 2005.
15. J. H. Min and Y.-C. Lee, "Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters", Exp. Syst. Applicat., Vol. 28, no. 4, pp. 128-134, 2005.
16. Tsakonas, G. Dounias, M. Doumpos, and C. Zopounidis, "Bank-ruptcy prediction with neural logit networks by means of grammar-guided genetic programming", Exp. Syst. Applicat., Vol. 30, no. 3, pp. 449-461, 2006.
17. E. Alfaro, N. Garcia, M. Gamez, and D. Elizondo, "Bankruptcy forecasting: An empirical comparison of AdaBoost and neural net-works", Decision Support Syst., Vol. 45, no. 1, pp. 110-122, 2008.
18. L. Nanni, and A. Lumini, "An experimental comparison of ensem-ble of classifiers for bankruptcy prediction and credit scoring", Exp. Syst. Applicat., Vol. 36, no. 2, pp. 3028-3033, 2009.
19. F.-M. Tseng and Y.-C. Hu, "Comparing four bankruptcy prediction models: Logit, quadratic interval logit, neural and fuzzy neural networks", Exp. Syst. Applicat., Vol. 37, no. 3, pp. 1846-1853, 2010.
20. S. Finlay, "Multiple classifier architectures and their application to credit risk assessment", Euro. J. Operation. Res., Vol. 210, no. 2, pp. 368-378, 2011.
21. H. Li and J. Sun, "Forecasting business failure: The use of nearest-neighbour support vectors and correcting imbalanced samples-Evidence from the Chinese hotel industry", Tourism Manag., Vol. 33, no. 3, pp. 622-634, 2012.
22. S.T. Ng, J. M. W. Wong, and J. Zhang, "Applying Z-score model to distinguish insolvent construction companies in China", Habitat Int., Vol. 35, no. 4, pp. 599-607, 2011.
23. H. P. Tserng, G.-F. Lin, L. K. Tsai and P.-C. Chen, "An enforced support vector machine model for construction contractor default prediction", Automat. Construct., Vol. 20(8), pp. 1242-1249, 2011.
24. M. Horta, A. S. Camanho and M. Costa, "Performance assessment of construction companies: A study of factors promoting financial soundness and innovation in the industry", Int. J. Product. Econ., Vol. 137(1), pp. 84-93, 2012.
25. J.L. McClelland, D.E. Rumelhart, and PDP Research Group, "Par-allel Distributed Processing: Psychological and Biological Mod-els", MIT Press. 1986.
26. R. Hecht-Nielsen, "Neurocomputing", MA: Addison-Wesley. 1990.
27. P.C. Pendharkar, "A threshold varying artificial neural network approach for classification and its application to bankruptcy predic-tion problem", Comput. Operat. Res., Vol. 32, pp. 2561-2582, 2005.
28. R. E. Schapire, "The strength of weak learnability", Machine Learning, Vol. 5(2), pp. 197-227, 1990.
29. Freund, Y. Boosting a weak algorithm by majority. Informat. Com-putat., Vol.121(2), pp. 256-285, 1995.
30. H. Drucker, R. Schapire and P. Simard, "Improving performance in neural networks using boosting algorithm", In: S. J. Hanson, J. D. Cowan, and C. L. Giles (Eds.), Adv. Neural Informat. Process. Syst., pp.42-49, 1993.
31. Y. Freund and R.E. Schapire. A decision-theoretic generalization of on-line learning and an application to boosting. In: Proceedings of the 2nd European Conference on Computational Learning Theory, Barcelona, Spain, 1995, pp. 23-37.
32. L. Breiman, "Bagging predictors", Machine Learning, Vol. 24, no. 2, pp. 123-140, 1996.
33. L. Breiman, "Randomizing outputs to increase prediction accu-racy", Machine Learning, Vol. 40, no. 3, pp. 229-242, 2000.
34. E.I. Altman, "Predicting financial distress of companies: Revisiting the Z-score and Zeta models", Working paper, http://pages.stern.nyu.edu/~ealtman/Zscores.pdf, 2000.
35. B. Shi, C. Lu and L. Chen, "Back-propagation neural network with enhanced neuron characteristics", U.S. Patent 6, 876, 989, Aug 20, 2005.
36. M. Moussa, A. Savich and S. Areibi, "Architecture, system and method for artificial neural network implementation", U.S. Patent 8, 103, 606, Jan 24, 2012.
37. D.D. Tirunagari, "System and method for effective caching using neural networks", U.S. Patent 2, 012, 041, 914, Feb 16, 2012.
38. B.Y. Chen, W.G. Hanley, T.D. Lemmond, L. J. Hiller, D.A. Knapp and M.J. Mugge, "Discriminant forest classification method and system", U.S. Patent 2, 009, 281, 981, Nov 12, 2009.
39. L. Zhao, L. Boroczky, L.A. Agnihotri and M.C. C. Lee, "Methods for feature selection using classifier ensemble based genetic algo-rithms", U.S. Patent 2, 010, 036, 782, Feb 11, 2010.
40. K. Yuta, "Method, program, and apparatus for generating two-class classification or prediction model", U.S. Patent 2, 010, 241, Sept 23, 2010.
41. R. Mise, "Pattern recognition apparatus and processing method thereof", U.S. Patent 20, 100, 296, 740, Nov 25, 2010.
42. S. Nakano and H. Sato, "Object identification apparatus and object identification method", U.S. Patent 20, 110, 103, 694, May 5, 2011.
43. K. Najarian and A. Darvish, "Automated methods and systems for the detection and identification of money service business transac-tions", U.S. Patent 8, 145, 585, Mar 27, 2012.
44. J. N. Bahnck, M. Larue, A. Blumetti, L. Rose, B. Crigler and S. Stoker, "Method and system for dynamically producing detailed trade payment experience for enhancing credit evaluation", WO Patent 2, 010, 111, 684, Sept 30, 2010.
45. H. Higashi, "Small-scale credit management system and small-scale credit management program", Japanese Patent 2, 011, 070, 331, Apr 7, 2011.