JOURNAL BROWSE
Search
Advanced SearchSearch Tips
Genetic Algorithm based Hybrid Ensemble Model
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Genetic Algorithm based Hybrid Ensemble Model
Min, Sung-Hwan;
  PDF(new window)
 Abstract
An ensemble classifier is a method that combines output of multiple classifiers. It has been widely accepted that ensemble classifiers can improve the prediction accuracy. Recently, ensemble techniques have been successfully applied to the bankruptcy prediction. Bagging and random subspace are the most popular ensemble techniques. Bagging and random subspace have proved to be very effective in improving the generalization ability respectively. However, there are few studies which have focused on the integration of bagging and random subspace. In this study, we proposed a new hybrid ensemble model to integrate bagging and random subspace method using genetic algorithm for improving the performance of the model. The proposed model is applied to the bankruptcy prediction for Korean companies and compared with other models in this study. The experimental results showed that the proposed model performs better than the other models such as the single classifier, the original ensemble model and the simple hybrid model.
 Keywords
Random Subspace;Bagging;Bankruptcy Prediction;Genetic Algorithms;
 Language
Korean
 Cited by
 References
1.
Altman, E. L., "Financial ratios, discriminant analysis and the prediction of corporate bankruptcy", The Journal of Finance, Vol. 23, No. 4, 1968, pp. 589-609. crossref(new window)

2.
Beaver, W., "Financial ratios as predictors of failure, empirical research in accounting : Selected studied", Journal of Accounting Research, Vol. 4, No. 3, 1966, pp. 71-111. crossref(new window)

3.
Breiman, L., "Bagging predictors", Machine Learning, Vol. 24, No. 2, 1996, pp. 123-140.

4.
Buta, P., "Mining for financial knowledge with CBR", AI Expert, Vol. 9, No. 10, 1994, pp. 34-41.

5.
Choi, H. N. and Lim, D. H., "Bankruptcy prediction using ensemble SVM model", Journal of the Korean Data and Information Sciences Society, Vol. 24, No. 6, 2013, pp. 1113-1125. crossref(new window)

6.
Dietterich, T. G., "Machine-learning research : Four current directions", AI Magazine, Vol. 18, No. 4, 1997, pp. 97-136.

7.
Freund, Y. and Schapire, R., "Experiments with a new boosting algorithm", Proceedings of the 13th International Conference on Machine learning, 1996, pp. 148-156.

8.
Hansen, L. and Salamon, P., "Neural network ensembles", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 12, No. 10, 1990, pp. 993-1001. crossref(new window)

9.
Ho, T., "The random subspace method for construction decision forests", IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, pp. 832-844.

10.
Kim, M., "A Performance Comparison of Ensemble in Bankruptcy Prediction", Entrue Journal of Information Technology, Vol. 8, No. 2, 2009, pp. 41-49.

11.
Kim, M., Kang, D., and Kim, H. B., "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, pp. 1074-1082. crossref(new window)

12.
Kim, S. H. and Kim, J. W., "SOHO Bankruptcy Prediction Using Modified Bagging Predictors", Journal of Intelligence and Information Systems, Vol. 13, No. 2, 2007, pp. 15-26.

13.
Kuncheva, L. I. and Whitaker, C. J., "Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy", Machine Learning, Vol. 51, No. 2, 2003, pp. 181-207. crossref(new window)

14.
Li, H., Lee, Y.-C., Zhou, Y. C., and Sun, J., "The random subspace binary logit(RSBL) model for bankruptcy prediction", Knowledge-Based Systems, Vol. 24, No. 8, 2011, pp. 1380-1388. crossref(new window)

15.
Marques, A. I., Garcia, V., and Sanchez, J. S., "Two-Level Classifier Ensembles for Credit Risk Assessment", Expert Systems with Applications, Vol. 39, No. 12, 2012, pp. 10916-10922. crossref(new window)

16.
Messier, W. F. Jr., and Hansen, J. V., "Inducing rules for expert system development : an example using default and bankruptcy data", Management Science, Vol. 34, No. 12, 1998, pp. 1403-1415.

17.
Meyer, P. A. and Pifer, H., "Prediction of bank failures", The Journal of Finance, Vol. 25, 1970, pp. 853-868. crossref(new window)

18.
Min, S., "Developing an Ensemble Classifier for Bankruptcy Prediction", Journal of the Korea Industrial Information Systems Research, Vol. 17, No. 7, 2012, pp. 139-148. crossref(new window)

19.
Min, S., "Bankruptcy Prediction Using an Improved Bagging Ensemble", Journal of Intelligence and Information Systems, Vol. 20, No. 4, 2014, pp. 121-139.

20.
Ohlson, J., "Financial ratios and the probabilistic prediction of bankruptcy", Journal of Accounting Research, Vol. 18, No. 1, 1980, pp. 109-131. crossref(new window)

21.
Tam, K. Y. and Kiang, M. Y., "Managerial applications of neural networks : the case of bank failure predictions", Management Science, Vol. 38, No. 7, 1992, pp. 926-947. crossref(new window)

22.
Zhang, G., Hu, Y. M., Patuwo, E. B., and Indro, C. D., "Artificial neural networks in bankruptcy prediction : general framework and cross-validation analysis", European Journal of Operational Research, Vol. 116, 1999, pp. 16-32. crossref(new window)