AdaBoost based bankruptcy forecasting of Korean construction companies

Applied Soft Computing Journal

Volumn 24, Issue , 2014, Pages 494-499

  Heo, Junyoung ^a   Yang, Jin Yong ^a  

^a Hansung University   (South Korea)

Author keywords

AdaBoost; Bankruptcy; Z score

Indexed keywords

CONSTRUCTION; CONSTRUCTION INDUSTRY; FINANCE; FORECASTING;

APPROPRIATE MODELS; BANKRUPTCY; BANKRUPTCY PREDICTION; CAPITAL STRUCTURE; CONSTRUCTION COMPANIES; FORECASTING MODELING; PREDICTIVE ABILITIES; Z-SCORES;

ADAPTIVE BOOSTING;

EID: 84907345291     PISSN: 15684946     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.asoc.2014.08.009     Document Type: Article

References (17)

1. CERIK(Construction Economy Research Institute of Korea) 2013 Construction Market Outlook Report 2012
2. J. Sun, B. Liao, and H. Li AdaBoost and bagging ensemble approaches with neural network as base learner for financial distress prediction of chinese construction and real estate companies Recent Pat. Comput. Sci. 6 2013 47 59
3. E.I. Altman Predicting Financial Distress of Companies: Revisiting the Z-score and ZETA Models 2000 Stern School of Business, New York University 9 12
4. E. Alfaro et al. Bankruptcy forecasting: an empirical comparison of AdaBoost and neural networks Decis. Support Syst. 45. 1 2008 110 122
5. J.H. Min, and Y.-C. Lee Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters Expert Syst. Appl. 28 4 2005 603 614 (Pubitemid 40583838)
6. K.-S. Shin, T.S. Lee, and H.-j. Kim An application of support vector machines in bankruptcy prediction model Expert Syst. Appl. 28. 1 2005 127 135
7. R.L. Wilson, and R. Sharda Bankruptcy prediction using neural networks Decis. Support Syst. 11 5 1994 545 557
8. A. Verikas et al. Hybrid and ensemble-based soft computing models in bankruptcy prediction: a survey Soft Comput. 14 9 2010 995 1010
9. NICE DnB, http://www.nicednb.com.
10. Y. Freund, and R.E. Schapire A desicion-theoretic generalization of on-line learning and an application to boosting Computational Learning Theory 1995 Springer Berlin Heidelberg
11. Neurolab, https://code.google.com/p/neurolab/.
12. Pedregosa et al. Scikit-learn: machine learning in python JMLR 12 2011 2825 2830
13. M.-J. Kim, and D.-K. Kang Ensemble with neural networks for bankruptcy prediction Expert Syst. Appl. 37 4 2010 3373 3379
14. A.A. Kasgari et al. Prediction of bankruptcy Iranian corporations through artificial neural network and probit-based analyses Neural Comput. Appl. 23 3-4 2013 927 936
15. L. Zhou, K.K. Lai, and J. Yen Bankruptcy prediction using SVM models with a new approach to combine features selection and parameter optimisation Int. J. Syst. Sci. 45 3 2014 241 253
16. B.E. Erdogan Prediction of bankruptcy using support vector machines: an application to bank bankruptcy J. Stat. Comput. Simul. 83 8 2013 1543 1555
17. N. Sharma, N. Arun, and V. Ravi An ant colony optimisation and Nelder-Mead simplex hybrid algorithm for training neural networks: an application to bankruptcy prediction in banks Int. J. Inform. Decis. Sci. 5 2 2013 188 203