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http://dx.doi.org/10.9716/KITS.2016.15.1.153

Randomized Bagging for Bankruptcy Prediction  

Min, Sung-Hwan (한림대학교 경영학과)
Publication Information
Journal of Information Technology Services / v.15, no.1, 2016 , pp. 153-166 More about this Journal
Abstract
Ensemble classification is an approach that combines individually trained classifiers in order to improve prediction accuracy over individual classifiers. Ensemble techniques have been shown to be very effective in improving the generalization ability of the classifier. But base classifiers need to be as accurate and diverse as possible in order to enhance the generalization abilities of an ensemble model. Bagging is one of the most popular ensemble methods. In bagging, the different training data subsets are randomly drawn with replacement from the original training dataset. Base classifiers are trained on the different bootstrap samples. In this study we proposed a new bagging variant ensemble model, Randomized Bagging (RBagging) for improving the standard bagging ensemble model. The proposed model was applied to the bankruptcy prediction problem using a real data set and the results were compared with those of the other models. The experimental results showed that the proposed model outperformed the standard bagging model.
Keywords
Bagging; Bankruptcy Prediction; Ensemble;
Citations & Related Records
Times Cited By KSCI : 5  (Citation Analysis)
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1 Altman, E.L., "Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy", The Journal of Finance, Vol. 23, No.4, 1968, 589-609.   DOI
2 Beaver, W., "Financial Ratios as Predictors of Failure, Empirical Research in Accounting : Selected Studied", Journal of Accounting Research, Vol.4, No.3, 1966, 71-111.   DOI
3 Bian, S. and W. Wang, "On Diversity and Accuracy of Homogeneous and Heterogeneous Ensembles", International Journal of Hybrid Intelligent Systems, Vol.4, No.2, 2007, 103-128.   DOI
4 Breiman, L., "Bagging Predictors", Machine Learning, Vol.24, No.2, 1996, 123-140.   DOI
5 Bryant, S.M., "A Case-Based Reasoning Approach to Bankruptcy Prediction Modeling", Intelligent Systems in Accounting, Finance and Management, Vol.6, No.3, 1997, 195-214.   DOI
6 Choi, H.N. and D.H. Lim, "Bankruptcy Prediction Using Ensemble SVM Model", Journal of the Korean Data and Information Science Society, Vol.24, No.6, 2013, 1113-1125. (최하나, 임동훈, "앙상블 SVM 모형을 이용한 기업부도 예측", 한국데이터정보과학회지, 제24권 제6호, 2013, 1113-1125.)   DOI
7 Dietterich, T.G., "Machine-Learning Research : Four Current Directions", AI Magazine, Vol.18, No.4, 1997, 97-136.
8 Freund, Y. and R. Schapire, "Experiments with a New Boosting Algorithm", Proceedings of the 13th International Conference on Machine learning, 1996, 148-156.
9 Kim, J.W. and W.C. Jhee, "Credit Card Bad Debt Prediction Model based on Support Vector Machine", Journal of Information Technology Services, Vol.11, No.4, 2012, 233-250. (김진우, 지원철, "신용카드 대손회원 예측을 위한 SVM 모형", 한국IT서비스학회지, 제11권, 제4호, 2012, 233-250.)   DOI
10 Kim, M.J., "A Performance Comparison of Ensemble in Bankruptcy Prediction", Entrue Journal of Information Technology, Vol.8, No.2, 2009, 41-49. (김명종, "기업부실화 예측에 대한 앙상블 학습의 성과 비교", 엔트루 저널, 제8권, 제2호, 2009, 41-49.)
11 Kim, M.J., D.K. Kang, and H.B. Kim, "Geometric Mean Based Boosting Algorithm with Over-Sampling to Resolve Data Imbalance Problem for Bankruptcy Prediction", Expert Systems with Applications, Vol.42, No.3, 2015, 1074-1082.   DOI
12 Kim, S.H. and J.W. Kim, "SOHO Bankruptcy Prediction Using Modified Bagging Predictors", Journal of Intelligence and Information Systems, Vol.13, No.2, 2007, 15-26. (김승혁, 김종우, "Modified Bagging Predictors를 이용한 SOHO 부도 예측", 한국지능정보시스템학회논문지, 제13권, 제2호, 2007, 15-26.)
13 Kuncheva, L.I. and C.J. Whitaker, "Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy", Machine Learning, Vol.51, No.2, 2003, 181-207.   DOI
14 Li, H. Y.C. Lee, Y.C. Zhou, and J. Sun, "The Random Subspace Binary Logit (RSBL) Model for Bankruptcy Prediction", Knowledge-Based Systems, Vol.24, No.8, 2011, 1380-1388.   DOI
15 Lu, Y., N.Y. Zeng, X.H. Liu, and S.J. Yi, "A New Hybrid Algorithm for Bankruptcy Prediction Using Switching Particle Swarm Optimization and Support Vector Machines", Discrete Dynamics in Nature and Society, Vol. 2015, 2015 http://dx.doi.org/10.1155/2015/294930(Downloaded February 19, 2016.)   DOI
16 Meyer, P.A. and H. Pifer, "Prediction of Bank Failures", The Journal of Finance, Vol.25, 1970, 853-868.   DOI
17 Min, S.H., "Bankruptcy Prediction Using an Improved Bagging Ensemble", Journal of Intelligence and Information Systems, Vol.20, No.4, 2014, 121-139. (민성환, "개선된 배깅 앙상블을 활용한 기업부도예측", 지능정보연구, 제20권, 제4호, 2014, 121-139.)   DOI
18 Min, S., "Optimization of Random Subspace Ensemble for Bankruptcy Prediction", Journal of Information Technology Services, Vol.14, No.4, 2015, 121-135. (민성환, "재무부실화 예측을 위한 랜덤 서브스페이스 앙상블 모형의 최적화", 한국IT서비스학회지, 제14권, 제4호, 2015, 121-135.)
19 Ohlson, J., "Financial ratios and the probabilistic prediction of bankruptcy", Journal of Accounting Research, Vol.18, No.1, 1980, 109-131.   DOI
20 Shaw, M.J. and J.A. Gentry, "Using and Expert System with Inductive Learning to Evaluate Business Loans", Financial Management, Vol.17, No.3, 1988, 45-56.   DOI
21 Yu, L., "Credit Risk Evaluation with a Least Squares Fuzzy Support Vector Machines Classifier", Discrete Dynamics in Nature and Society, 2014.
22 Zhang, G., Y.M. Hu, E.B., Patuwo, and C. D. Indro, "Artificial Neural Networks in Bankruptcy Prediction : General Framework and Cross-Validation Analysis", European Journal of Operational Research, Vol.116, 1999, 16-32.   DOI
23 Zhang, Y.D., S.H. Wang, and G.L. Ji, "A Rule-Based Model for Bankruptcy Prediction Based on an Improved Genetic Ant Colony Algorithm", Mathematical Problems in Engineering, 2013.