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Volumn 36, Issue 3 PART 1, 2009, Pages 5297-5303

A selective ensemble based on expected probabilities for bankruptcy prediction

Author keywords

Bankruptcy prediction; Classifier ensembles; Selective ensembles

Indexed keywords

BACKPROPAGATION; DECISION TREES; NEURAL NETWORKS;

EID: 58349100252     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2008.06.068     Document Type: Article
Times cited : (119)

References (32)
  • 1
    • 84980104458 scopus 로고
    • Financial ratios, discriminant analysis and the prediction of corporate bankruptcy
    • Altman E.J. Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance 23 4 (1968) 589-609
    • (1968) Journal of Finance , vol.23 , Issue.4 , pp. 589-609
    • Altman, E.J.1
  • 2
    • 43949151078 scopus 로고
    • Corporate distress diagnosis: comparisons using linear discriminant analysis and neural networks
    • Altman E.I., Marco G.V., and Varetto F. Corporate distress diagnosis: comparisons using linear discriminant analysis and neural networks. Journal of Banking and Finance 18 (1994) 505-529
    • (1994) Journal of Banking and Finance , vol.18 , pp. 505-529
    • Altman, E.I.1    Marco, G.V.2    Varetto, F.3
  • 3
    • 58349114392 scopus 로고    scopus 로고
    • Balcaen, S., & Ooghe, H. (2004). 35 years of studies on business failure: an overview of the classical statistical methodologies and their related problems. Working Paper, No. 248, Department of Accountancy and Corporate Finance, Ghent University, Belgium.
    • Balcaen, S., & Ooghe, H. (2004). 35 years of studies on business failure: an overview of the classical statistical methodologies and their related problems. Working Paper, No. 248, Department of Accountancy and Corporate Finance, Ghent University, Belgium.
  • 4
    • 0032645080 scopus 로고    scopus 로고
    • An empirical comparison of voting classification algorithms: Bagging, boosting, and variants
    • Bauer E., and Kohavi R. An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine Learning 36 1-2 (1999) 105-139
    • (1999) Machine Learning , vol.36 , Issue.1-2 , pp. 105-139
    • Bauer, E.1    Kohavi, R.2
  • 5
    • 0002554419 scopus 로고    scopus 로고
    • Financial ratios as predictors of failure
    • Beaver W. Financial ratios as predictors of failure. Journal of Accounting Research 4 (1996) 71-111
    • (1996) Journal of Accounting Research , vol.4 , pp. 71-111
    • Beaver, W.1
  • 6
    • 0030211964 scopus 로고    scopus 로고
    • Bagging predictors
    • Breiman L. Bagging predictors. Machine Learning 24 2 (1996) 123-140
    • (1996) Machine Learning , vol.24 , Issue.2 , pp. 123-140
    • Breiman, L.1
  • 8
    • 84977404808 scopus 로고
    • Pitfalls in the application of discriminant analysis in business, finance and economics
    • Eisenbeis R.A. Pitfalls in the application of discriminant analysis in business, finance and economics. Journal of Finance (1977) 75-900
    • (1977) Journal of Finance , pp. 75-900
    • Eisenbeis, R.A.1
  • 9
    • 0031211090 scopus 로고    scopus 로고
    • A decision-theoretic generalization of on-line learning and an application to boosting
    • Freund Y., and Schapire R.E. A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences 55 1 (1997) 119-139
    • (1997) Journal of Computer and System Sciences , vol.55 , Issue.1 , pp. 119-139
    • Freund, Y.1    Schapire, R.E.2
  • 10
    • 58349084615 scopus 로고    scopus 로고
    • Hung, C., Chen, J.-H., Wermter, S. (2007). Hybrid probability-based ensembles for bankruptcy prediction. In Proceedings of international conference on business and information. July 11-13, Tokyo, Japan.
    • Hung, C., Chen, J.-H., Wermter, S. (2007). Hybrid probability-based ensembles for bankruptcy prediction. In Proceedings of international conference on business and information. July 11-13, Tokyo, Japan.
  • 11
    • 0031199479 scopus 로고    scopus 로고
    • Bankruptcy prediction using case-based reasoning, neural networks, and discriminant analysis
    • Jo H., Han I., and Lee H. Bankruptcy prediction using case-based reasoning, neural networks, and discriminant analysis. Expert Systems with Applications 13 2 (1997) 97-108
    • (1997) Expert Systems with Applications , vol.13 , Issue.2 , pp. 97-108
    • Jo, H.1    Han, I.2    Lee, H.3
  • 12
    • 0346591081 scopus 로고    scopus 로고
    • A neural network approach to the prediction of going concern status
    • Koh H.C., and Tan S.S. A neural network approach to the prediction of going concern status. Accounting and Business Research 29 3 (1999) 211-216
    • (1999) Accounting and Business Research , vol.29 , Issue.3 , pp. 211-216
    • Koh, H.C.1    Tan, S.S.2
  • 13
    • 0001290841 scopus 로고    scopus 로고
    • Glossary of terms
    • Kohavi R., and Foster P. Glossary of terms. Machine Learning 30 23 (1998) 271-274
    • (1998) Machine Learning , vol.30 , Issue.23 , pp. 271-274
    • Kohavi, R.1    Foster, P.2
  • 15
    • 17844363481 scopus 로고    scopus 로고
    • Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters
    • Min J.H., and Lee Y.-C. Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters. Expert Systems with Applications 28 4 (2005) 603-614
    • (2005) Expert Systems with Applications , vol.28 , Issue.4 , pp. 603-614
    • Min, J.H.1    Lee, Y.-C.2
  • 17
    • 0025559251 scopus 로고    scopus 로고
    • Odom, M., & Sharda, R. (1990). A neural network model for bankruptcy prediction. In Proceedings of international joint conference on neural networks (pp. 163-168).
    • Odom, M., & Sharda, R. (1990). A neural network model for bankruptcy prediction. In Proceedings of international joint conference on neural networks (pp. 163-168).
  • 18
    • 0000666375 scopus 로고
    • Financial ratios and the probabilistic prediction of bankruptcy
    • Ohlson J. Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research 18 1 (1980) 109-131
    • (1980) Journal of Accounting Research , vol.18 , Issue.1 , pp. 109-131
    • Ohlson, J.1
  • 19
    • 0003120218 scopus 로고    scopus 로고
    • Fast training of support vector machines using sequential minimal optimization
    • Schoelkopf B., Burges C.J.C., and Smola A.J. (Eds), MIT Press, Cambridge, MA
    • Platt J. Fast training of support vector machines using sequential minimal optimization. In: Schoelkopf B., Burges C.J.C., and Smola A.J. (Eds). Advances in kernel methods - support vector learning (1999), MIT Press, Cambridge, MA 185-208
    • (1999) Advances in kernel methods - support vector learning , pp. 185-208
    • Platt, J.1
  • 21
    • 33744584654 scopus 로고
    • Induction of decision trees
    • Quinlan R. Induction of decision trees. Machine Learning 1 1 (1986) 81-106
    • (1986) Machine Learning , vol.1 , Issue.1 , pp. 81-106
    • Quinlan, R.1
  • 24
    • 9244259665 scopus 로고    scopus 로고
    • An application of support vector machines in bankruptcy prediction model
    • Shin K.-S., Lee T.S., and Kim H.-J. An application of support vector machines in bankruptcy prediction model. Expert Systems with Applications 28 1 (2005) 127-135
    • (2005) Expert Systems with Applications , vol.28 , Issue.1 , pp. 127-135
    • Shin, K.-S.1    Lee, T.S.2    Kim, H.-J.3
  • 25
    • 84997479670 scopus 로고
    • Managerial applications of neural networks: The case of bank
    • Tam K., and Kiang M. Managerial applications of neural networks: The case of bank. Management Science 38 7 (1992) 926-947
    • (1992) Management Science , vol.38 , Issue.7 , pp. 926-947
    • Tam, K.1    Kiang, M.2
  • 26
    • 38649113538 scopus 로고    scopus 로고
    • Using neural network ensembles for bankruptcy prediction and credit scoring
    • Tsai C.-F., and Wu J.-W. Using neural network ensembles for bankruptcy prediction and credit scoring. Expert Systems with Applications 36 2 (2008)
    • (2008) Expert Systems with Applications , vol.36 , Issue.2
    • Tsai, C.-F.1    Wu, J.-W.2
  • 27
    • 34249753618 scopus 로고
    • Support-vector networks
    • Vapnik V. Support-vector networks. Machine Learning 20 3 (1995) 273-297
    • (1995) Machine Learning , vol.20 , Issue.3 , pp. 273-297
    • Vapnik, V.1
  • 28
    • 13544268431 scopus 로고    scopus 로고
    • Neural network ensemble strategies for financial decision applications
    • West D., Dellana S., and Qian J. Neural network ensemble strategies for financial decision applications. Computers & Operations Research 32 (2005) 2543-2559
    • (2005) Computers & Operations Research , vol.32 , pp. 2543-2559
    • West, D.1    Dellana, S.2    Qian, J.3
  • 29
    • 58349112905 scopus 로고    scopus 로고
    • Wieslaw, P. (2004). Application of discrete predicting structures in an early warning expert system for financial distress. Ph.D. Thesis. Szczecin: Szczecin Technical University.
    • Wieslaw, P. (2004). Application of discrete predicting structures in an early warning expert system for financial distress. Ph.D. Thesis. Szczecin: Szczecin Technical University.
  • 30
    • 85014149758 scopus 로고    scopus 로고
    • Elsevier, Morgan Kaufmann Publishers 2005
    • Witten I.H., and Frank E. Data mining (2005), Elsevier, Morgan Kaufmann Publishers 2005
    • (2005) Data mining
    • Witten, I.H.1    Frank, E.2
  • 31
    • 0036567392 scopus 로고    scopus 로고
    • Ensembling neural networks: Many could be better than all
    • Zhou Z.-H., Wu J., and Tang W. Ensembling neural networks: Many could be better than all. Artificial Intelligence 137 1-2 (2002) 239-263
    • (2002) Artificial Intelligence , vol.137 , Issue.1-2 , pp. 239-263
    • Zhou, Z.-H.1    Wu, J.2    Tang, W.3
  • 32
    • 0001953906 scopus 로고
    • Methodological issues related to the estimation of financial distress prediction models
    • Zmijewski M. Methodological issues related to the estimation of financial distress prediction models. Journal of Accounting Research 22 1 (1984) 59-82
    • (1984) Journal of Accounting Research , vol.22 , Issue.1 , pp. 59-82
    • Zmijewski, M.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.