Empirical analysis of support vector machine ensemble classifiers

Expert Systems with Applications

Volumn 36, Issue 3 PART 2, 2009, Pages 6466-6476

  Wang, Shi jin ^a   Mathew, Avin ^b   Chen, Yan ^c   Xi, Li feng ^a   Ma, Lin ^b   Lee, Jay ^c  

^a SHANGHAI JIAO TONG UNIVERSITY   (China)

^b QUEENSLAND UNIVERSITY OF TECHNOLOGY   (Australia)

^c UNIVERSITY OF CINCINNATI   (United States)

Author keywords

AdaBoost; Bagging; Ensemble classification; Support vector machines (SVMs)

Indexed keywords

ADAPTIVE BOOSTING; BENCHMARKING; CLASSIFICATION (OF INFORMATION); DECISION TREES;

BAGGING; CLASSIFICATION ACCURACY; ENSEMBLE CLASSIFICATION; ENSEMBLE CLASSIFIERS; INDUSTRIAL CASE STUDY; MACHINE LEARNING COMMUNITIES; POLYNOMIAL KERNELS; SUPPORT VECTOR MACHINE (SVMS);

SUPPORT VECTOR MACHINES;

EID: 58349119116     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2008.07.041     Document Type: Article

References (36)

1. Banfield R.E., Hall L.O., Bowyer K.W., and Kegelmeyer W.P. A comparison of decision tree ensemble creation techniques. IEEE Transactions on Pattern Analysis and Machine Intelligence 29 1 (2007) 173-180
2. Bauer E., and Kohavi R. An empirical comparison of voting classification algorithms: bagging, boosting and variants. Machine Learning 36 (1999) 105-139
3. Breiman L. Bagging predictors. Machine Learning 24 (1996) 123-140
4. Breiman L. Arcing classifiers. The Annals of Statistics 26 3 (1998) 801-849
5. Breiman L. Random forests. Machine Learning 45 (2001) 5-32
6. Burges C. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2 (1998) 121-167
7. Chang, C. C. & Lin, C. J. LIBSVM: A library for support vector machines. (2001). Software .
8. Chan J., Huang C., and DeFries R. Enhanced algorithm performance for land cover classification from remotely sensed data using bagging and boosting. IEEE Transactions on Geoscience and Remote Sensing 39 3 (2001) 693-695
9. Chen, Y., Wang, H. X. & Lee, J. A. (2007). New method for feature selection and gear defect detection. In ASME International Conference on Manufacturing Science & Engineering (MSEC), Atlanta, GA, US.
10. Cristianini N., and Shawe-Taylor J. A introduction to support vector machines and other kernel-based learning methods (2000), Cambridge University Press, Cambridge, UK
11. Eom, J. -H., Kim, S. -C & Zhang, B. -T. (in press). AptaCDSS-E: A classifier ensemble-based clinical decision support system for cardiovascular disease level prediction. Expert System with Application.
12. Freund Y. Boosting a weak learning algorithm by majority. Information and Computation 121 2 (1995) 256-285
13. Freund Y., and Schapire R. A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences 55 1 (1997) 119-139
14. Freund Y., and Schapire R. A short introduction to boosting. Journal of Japanese Society for Artificial Intelligence 14 5 (1999) 771-780
15. Gordon J.J., Towsey M.W., Hogan J.M., Mathews S.A., and Timms P. Improved prediction of bacterial transcription start sites. Bioinformatics 22 2 (2006) 142-148
16. Hsu C.-W., and Lin C.-J. A comparison of methods for multiclass support vector machines. IEEE Transactions on Neural Networks 13 2 (2002) 415-425
17. Hu Q., He Z., Zhang Z., and Zi Y. Fault diagnosis of rotating machinery based on improved wavelet package transform and SVMs ensemble. Mechanical Systems and Signal Processing 21 (2007) 688-705
18. Kim Y.-C., Pang S., Je H.-M., Kim D., and Bang S.-Y. Constructing support vector machine ensemble. Pattern Recognition 36 (2003) 2757-2767
19. Kubat M., Holte R., and Matwin S. Machine learning for the detection of oil spills in satellite radar images. Machine Learning 30 (1998) 195-215
20. Kuncheva L.I. Combining pattern classifiers: Methods and algorithms (2004), John Wiley & Sons Inc, Hoboken, New Jersey
21. Lei Z., Yang Y., and Wu Z. Ensemble of support vector machine for text-independent speaker recognition. International Journal of Computer Science and Network Security 6 5A (2006) 163-167
22. Li, X., Wang, L. & Sung, E. A (2005). Study of AdaBoost with SVM based weak learners. Neural Networks, 2005. IJCNN'05. In Proceedings of 2005 IEEE International Joint Conference on 2005 (Vol. 1, pp. 196-201).
23. Lin, H. -T, & Li, L. (2005). Novel distance-based SVM kernels for infinite ensemble learning. In Proceedingsof the 12th International Conference on Neural Information Processing (pp.761-766).
24. Opitz D., and Maclin R. Popular ensemble methods: An empirical study. Journal of Artificial Intelligence Research 11 (1999) 169-198
25. Pang S., Kim D., and Sung Y. Membership authentication in the dynamic group by face classification using SVM ensemble. Pattern Recognition Letter 24 (2003) 215-225
26. Schwenk H., and Bengio Y. Boosting neural network. Neural Computation 12 (2000) 1869-1887
27. UCI Machine Learning Repository. .
28. Valentini G. An experimental bias-variance analysis of SVM ensembles based on resampling techniques. IEEE Transactions on System, Man and Cybernetics-Part B: Cybernetics 35 6 (2005) 1252-1271
29. Valentini G., and Dietterich T. Bias-variance analysis of support vector machines for the development of SVM-based ensemble methods. Journal of Machine Learning Research 5 (2004) 725-775
30. Vapnik V. The nature of statistical learning theory. 2nd ed. (1997), Springer Verlag, New York
31. Webb G.I. MultiBoosting: A technique for combing boosting and wagging. Machine Learning 40 2 (2000) 159-196
32. Webb G.I., and Zheng Z. Multistrategy ensemble learning: Reducing error by combining ensemble learning technique. IEEE Transaction on Knowledge and Data Engineering 16 8 (2004) 980-991
33. Wezel M., and Potharst R. Improved customer choice predictions using ensemble methods. European Journal of Operational Research 181 (2007) 436-452
34. Widodo A., and Yang B.-S. Support vector machine in machine condition monitoring and fault diagnosis. Mechanical Systems and Signal Processing 21 (2007) 2560-2574
35. Widodo A., Yang B.-S., and Han T. Combination of independent component analysis and support vector machines for intelligent faults diagnosis of induction motors. Expert Systems with Application 32 (2007) 299-312
36. Zhang C.X., Zhang J.S., and Zhang G.Y. An efficient modified boosting method for solving classification problems. Journal of Computational and Applied Mathematics (2007) online