메뉴 건너뛰기




Volumn 2, Issue , 2010, Pages

Performance enhancement of SVM ensembles using genetic algorithms in bankruptcy prediction

Author keywords

Bankruptcy prediction tree; Decision machines; Ensemble; Genetic algorithm; Support vector coverage optimization

Indexed keywords


EID: 78149346079     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICACTE.2010.5579271     Document Type: Conference Paper
Times cited : (6)

References (30)
  • 1
    • 34547105779 scopus 로고    scopus 로고
    • Multiclass corporate failure prediction by AdaBoost.MI
    • Afaro, E., Gamez, M , & Garcia, N. (2007). Multiclass corporate failure prediction by AdaBoost.MI. Advanced Economic Research, 13,301-312.
    • (2007) Advanced Economic Research , vol.13 , pp. 301-312
    • Afaro, E.1    Gamez, M.2    Garcia, N.3
  • 2
    • 41149115573 scopus 로고    scopus 로고
    • Bankruptcy forecasting: An empirical comparison of AdaBooost and neural networks
    • Alfaro, E., Garcia, N., Gamez, M., & Elizondo, D. (2008). Bankruptcy forecasting: an empirical comparison of AdaBooost and neural networks. Decision Support Systems, 45, 110-122.
    • (2008) Decision Support Systems , vol.45 , pp. 110-122
    • Alfaro, E.1    Garcia, N.2    Gamez, M.3    Elizondo, D.4
  • 4
    • 0032645080 scopus 로고    scopus 로고
    • An empirical comparison of voting classification algorithms: Bagging, boosting, and variants.
    • Bauer, E., & Kohavi, R (1999). An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine Learning, 36,105-139.
    • (1999) Machine Learning , vol.36 , pp. 105-139
    • Bauer, E.1    Kohavi, R.2
  • 5
    • 0030211964 scopus 로고
    • Bagging predictors
    • Breiman, L. (1994). Bagging predictors. Machine Learning, 24(2), 123-140.
    • (1994) Machine Learning , vol.24 , Issue.2 , pp. 123-140
    • Breiman, L.1
  • 9
    • 58149321460 scopus 로고
    • Boosting a weak learning algorithm by majority
    • Freund, Y. (1995). Boosting a weak learning algorithm by majority. Information and Computation, 121(2),256-285.
    • (1995) Information and Computation , vol.121 , Issue.2 , pp. 256-285
    • Freund, Y.1
  • 10
    • 0025507176 scopus 로고
    • Neural network ensembles
    • Hansen, L., & Salamon, P. (1990). Neural network ensembles, IEEE Trans. PAMI, Vol. 12,993-1001.
    • (1990) IEEE Trans. PAMI , vol.12 , pp. 993-1001
    • Hansen, L.1    Salamon, P.2
  • 12
    • 33750503791 scopus 로고    scopus 로고
    • Fault diagnosis of rotating machinery based on improved wavelet package transform and SVMs ensemble.
    • Hu, Q., He, Z., Zhang, Z., and Zi, Y. (2007). Fault diagnosis of rotating machinery based on improved wavelet package transform and SVMs ensemble. Mechanical Systems and Signal Processing, 21, 688-705.
    • (2007) Mechanical Systems and Signal Processing , vol.21 , pp. 688-705
    • Hu, Q.1    He, Z.2    Zhang, Z.3    Zi, Y.4
  • 14
    • 10044288346 scopus 로고    scopus 로고
    • Toward global optimization of case-based reasoning systems for financial forecasting
    • Kim, K. (2004). Toward global optimization of case-based reasoning systems for financial forecasting, Applied Intelligence, 21,239-249.
    • (2004) Applied Intelligence , vol.21 , pp. 239-249
    • Kim, K.1
  • 15
    • 78149306145 scopus 로고    scopus 로고
    • A performance comparison of ensembles in bankruptcy prediction
    • Kim, M. J., (2009). A Performance Comparison of Ensembles in Bankruptcy Prediction, Entrue Journal of Information Technology, 8(2),41-49.
    • (2009) Entrue Journal of Information Technology , vol.8 , Issue.2 , pp. 41-49
    • Kim, M.J.1
  • 16
    • 78149286228 scopus 로고    scopus 로고
    • Hybrid genetic algorithm for classifier ensemble selection
    • Kim, Y. W, Oh, I. S (2007). Hybrid genetic algorithm for classifier ensemble selection, The KIPS Transaction: Part B, 14-B(5), 369-376.
    • (2007) The KIPS Transaction: Part B , vol.14 B , Issue.5 , pp. 369-376
    • Kim, Y.W.1    Oh, I.S.2
  • 18
    • 54949142207 scopus 로고    scopus 로고
    • Hybrid classification algorithms based on boosting and support vector machines
    • Maia, T T, Braga, A. P., & Carvalho, A. F. (2008). Hybrid classification algorithms based on boosting and support vector machines, Kybernetes, 37(9),1469-1491
    • (2008) Kybernetes , vol.37 , Issue.9 , pp. 1469-1491
    • Maia, T.T.1    Braga, A.P.2    Carvalho, A.F.3
  • 19
    • 33744925661 scopus 로고    scopus 로고
    • Hybrid genetic algorithms and support vector machines for bankruptcy prediction.
    • Min, S. H.; Lee, J. M., & Han, I. G. (2006). Hybrid genetic algorithms and support vector machines for bankruptcy prediction. Expert Systems with Applications, 31, 652-660.
    • (2006) Expert Systems with Applications , vol.31 , pp. 652-660
    • Min, S.H.1    Lee, J.M.2    Han, I.G.3
  • 20
    • 78149347898 scopus 로고    scopus 로고
    • Feature selection for ensembles: A hierarchical multi-objective genetic algorithm approach
    • Oliveira, L. S., Sabourin, R, Bortolozzi, F., & Suen, C.Y. (2003). Feature selection for ensembles: a hierarchical multi-objective genetic algorithm approach, ICDAR 2003.
    • (2003) ICDAR 2003
    • Oliveira, L.S.1    Sabourin, R.2    Bortolozzi, F.3    Suen, C.Y.4
  • 22
    • 0037233257 scopus 로고    scopus 로고
    • Membership authentication in the dynamic group by face classification using SVM ensemble
    • Pang, S., Kim, D., & Sung, Y. (2003). Membership authentication in the dynamic group by face classification using SVM ensemble, Pattern Recognition Letter, 24, 215-225.
    • (2003) Pattern Recognition Letter , vol.24 , pp. 215-225
    • Pang, S.1    Kim, D.2    Sung, Y.3
  • 23
    • 0345159806 scopus 로고
    • Putting it all together: Methods for combining neural networks
    • J. D. Cowan, G. Tesauro, & J. Alspector, (Eds.) San Mateo, CA: Morgan Kaufmann
    • Perrone, M. E. (1994). Putting it all together: Methods for combining neural networks. In J. D. Cowan, G. Tesauro, & J. Alspector, (Eds.), Advances in Neural Information Processing Systems, 6, (pp. 1188-1189). San Mateo, CA: Morgan Kaufmann.
    • (1994) Advances in Neural Information Processing Systems , vol.6 , pp. 1188-1189
    • Perrone, M.E.1
  • 25
    • 0025448521 scopus 로고
    • The strength of weak learnability
    • Schapire, R. E. (1990). The strength of weak learnability. Machine Learning, 5(2), 197-227.
    • (1990) Machine Learning , vol.5 , Issue.2 , pp. 197-227
    • Schapire, R.E.1
  • 26
    • 0024895461 scopus 로고
    • A note on genetic algorithms for large-scale feature selection
    • Siedlecki, W., & Sklanski, J. (1989) A note on genetic algorithms for large-scale feature selection, Pattern Recognition Letters, 10, 335-347.
    • (1989) Pattern Recognition Letters , vol.10 , pp. 335-347
    • Siedlecki, W.1    Sklanski, J.2
  • 27
    • 29144474463 scopus 로고    scopus 로고
    • An experimental bias-variance analysis of SVM ensembles based on resampling techniques
    • Valentini, G. (2005). An experimental bias-variance analysis of SVM ensembles based on resampling techniques. IEEE Transactions on Systems, Man and Cybernetics-Part B: Cybernetics, 35(6), 1252-1271.
    • (2005) IEEE Transactions on Systems, Man and Cybernetics-Part B: Cybernetics , vol.35 , Issue.6 , pp. 1252-1271
    • Valentini, G.1
  • 28
    • 26944501740 scopus 로고    scopus 로고
    • Bias-variance analysis of support vector machines for the development of SVM-based ensemble methods
    • Valentini, G., & Dietterich, T (2004). Bias-variance analysis of support vector machines for the development of SVM-based ensemble methods, Journal of Machine Learning Research, 5, 725-775.
    • (2004) Journal of Machine Learning Research , vol.5 , pp. 725-775
    • Valentini, G.1    Dietterich, T.2
  • 30
    • 0036567392 scopus 로고    scopus 로고
    • Ensembling neural networks: Many could better than all
    • Zhou, Z. H. Wu, J. x., & Tang, W. (2002). Ensembling neural networks: many could better than all, Artificial Intelligence, 137, 239-263.
    • (2002) Artificial Intelligence , vol.137 , pp. 239-263
    • Zhou, Z.H.1    Wu, J.X.2    Tang, W.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.