A Robust AdaBoost.RT Based Ensemble Extreme Learning Machine

Mathematical Problems in Engineering

Volumn 2015, Issue , 2015, Pages

  Zhang, Pengbo ^a   Yang, Zhixin ^a  

^a UNIVERSITY OF MACAU   (Macao)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; APPROXIMATION ALGORITHMS; ARTIFICIAL INTELLIGENCE; BIG DATA; KNOWLEDGE ACQUISITION; LEARNING SYSTEMS; REGRESSION ANALYSIS;

APPROXIMATION ACCURACY; EFFECTIVE LEARNING; EXPERIMENTAL VERIFICATION; EXTREME LEARNING MACHINE; GENERALIZATION PERFORMANCE; HYBRID MACHINE LEARNING; REGRESSION APPLICATIONS; STATE-OF-THE-ART ALGORITHMS;

LEARNING ALGORITHMS;

EID: 84935846839     PISSN: 1024123X     EISSN: 15635147     Source Type: Journal    
DOI: 10.1155/2015/260970     Document Type: Article

References (37)

1. C. L. Philip Chen, J. Wang, C. H. Wang, and L. Chen, "A new learning algorithm for a Fully Connected Fuzzy Inference System (F-CONFIS)," IEEE Transactions on Neural Networks and Learning Systems, vol. 25, no. 10, pp. 1741-1757, 2014.
2. Z. Yang, P. K. Wong, C. M. Vong, J. Zhong, and J. Y. Liang, "Simultaneous-fault diagnosis of gas turbine generator systems using a pairwise-coupled probabilistic classifier," Mathematical Problems in Engineering, vol. 2013, Article ID 827128, 13 pages, 2013.
3. G.-B. Huang, Q.-Y. Zhu, and C.-K. Siew, "Extreme learning machine: theory and applications," Neurocomputing, vol. 70, no. 1-3, pp. 489-501, 2006.
4. G.-B. Huang, H. Zhou, X. Ding, and R. Zhang, "Extreme learning machine for regression and multiclass classification," IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 42, no. 2, pp. 513-529, 2012.
5. S. C. Ng, C. C. Cheung, and S. H. Leung, "Magnified gradient function with deterministic weight modification in adaptive learning," IEEE Transactions on Neural Networks, vol. 15, no. 6, pp. 1411-1423, 2004.
6. C. Cortes and V. Vapnik, "Support-vector networks," Machine Learning, vol. 20, no. 3, pp. 273-297, 1995.
7. H. J. Rong, G. B. Huang, N. Sundararajan, and P. Saratchandran, "Online sequential fuzzy extreme learningmachine for function approximation and classification problems," IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 39, no. 4, pp. 1067-1072, 2009.
8. J. Cao, Z. Lin, and G. B. Huang, "Voting base online sequential extreme learning machine for multi-class classification," in Proceedings of the IEEE International Symposiumon Circuits and Systems (ISCAS '13), pp. 2327-2330, IEEE, 2013.
9. J. Cao, Z. Lin, G. B. Huang, and N. Liu, "Voting based extreme learning machine," Information Sciences, vol. 185, no. 1, pp. 66-77, 2012.
10. N. Y. Liang, G. B. Huang, P. Saratchandran, and N. Sundararajan, "A fast and accurate online sequential learning algorithm for feedforward networks," IEEE Transactions on Neural Networks, vol. 17, no. 6, pp. 1411-1423, 2006.
11. J. Luo, C. M. Vong, and P. K. Wong, "Sparse Bayesian extreme learningmachine formulti-classification," IEEE Transactions on Neural Networks and Learning Systems, vol. 25, no. 4, pp. 836-843, 2014.
12. O. Chapelle, B. Scholkopf, and A. Zien, Semi-Supervised Learning, vol. 2, MIT Press, Cambridge, UK, 2006.
13. Y. Lan, Y. C. Soh, and G. B. Huang, "Ensemble of online sequential extreme learningmachine," Neurocomputing, vol. 72, no. 13, pp. 3391-3395, 2009.
14. N. Liu and H. Wang, "Ensemble based extreme learning machine," IEEE Signal Processing Letters, vol. 17, no. 8, pp. 754-757, 2010.
15. X. Xue, M. Yao, Z. Wu, and J. Yang, "Genetic ensemble of extreme learningmachine,"Neurocomputing, vol. 129, no. 10, pp. 175-184, 2014.
16. X. L. Wang, Y. Y. Chen, H. Zhao, and B. L. Lu, "Parallelized extreme learning machine ensemble based on min-max modular network," Neurocomputing, vol. 128, pp. 31-41, 2014.
17. B. V. Dasarathy and B. V. Sheela, "A composite classifier system design: concepts andmethodology," Proceedings of the IEEE, vol. 67, no. 5, pp. 708-713, 1979.
18. L. K. HansenandP. Salamon, "Neuralnetworkensembles," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, no. 10, pp. 993-1001, 1990.
19. L. Breiman, "Bagging predictors,"Machine Learning, vol. 24, no. 2, pp. 123-140, 1996.
20. R. E. Schapire, "The strength of weak learnability," Machine Learning, vol. 5, no. 2, pp. 197-227, 1990.
21. R. E. Schapire and Y. Freund, Boosting: Foundations and Algorithms/Robert E. Schapire, Yoav Freund, MIT Press, Cambridge, Mass, USA, 2012.
22. L. Breiman, "Random forests," Machine Learning, vol. 45, no. 1, pp. 5-32, 2001.
23. R. A. Jacobs, M. I. Jordan, S. J. Nowlan, and G. E. Hinton, "Adaptive mixtures of local experts," Neural Computation, vol. 3, no. 1, pp. 79-87, 1991.
24. V. Guruswami and A. Sahai, "Multiclass learning, boosting, and error-correcting codes," in Proceedings of the 12th Annual Conference on Computational Learning Theory (COLT 99), pp. 145-155, July 1999.
25. R. E. Schapire and Y. Singer, "Improved boosting algorithms using confidence-rated predictions," Machine Learning, vol. 37, no. 3, pp. 297-336, 1999.
26. N. Li, "Multiclass boosting with repartitioning," in Proceedings of the 23rd International Conference on Machine Learning, pp. 569-576, ACM, June 2006.
27. H. Drucker, "Improving regressors using boosting," in Proceedings of the 14th International Conference on Machine Learning, pp. 107-115, 1997.
28. R. Avnimelech and N. Intrator, "Boosting regression estimators," Neural Computation, vol. 11, no. 2, pp. 499-520, 1999.
29. D. P. Solomatine and D. L. Shrestha, "AdaBoost. RT: a boosting algorithm for regression problems," in Proceedings of the IEEE International Joint Conference on Neural Networks, vol. 2, pp. 1163-1168, IEEE, 2004.
30. D. L. Shrestha and D. P. Solomatine, "Experiments with AdaBoost. RT, an improved boosting scheme for regression," Neural Computation, vol. 18, no. 7, pp. 1678-1710, 2006.
31. H.-X. Tian and Z.-Z. Mao, "An ensemble ELM based on modified AdaBoost. RT algorithm for predicting the temperature of molten steel in ladle furnace," IEEE Transactions on Automation Science and Engineering, vol. 7, no. 1, pp. 73-80, 2010.
32. J. W. Cao, T. Chen, and J. Fan, "Fast online learning algorithm for landmark recognition based on BoW framework," in Proceedings of the 9th IEEE Conference on Industrial Electronics and Applications, Hangzhou, China, June 2014.
33. J. Cao, Z. Lin, and G.-B. Huang, "Composite function wavelet neural networks with extreme learning machine," Neurocomputing, vol. 73, no. 7-9, pp. 1405-1416, 2010.
34. R. Feely, Predicting stockmarket volatility using neural networks, B. A. (Mod. ) Ph. D. thesis], Trinity College Dublin, Dublin, Ireland, 2000.
35. K. Bache and M. Lichman, UCI Machine Learning Repository, University of California, School of Information and Computer Science, Irvine, Calif, USA, 2014, http://archive.ics.uci.edu/ml.
36. H. Drucker, C. J. Burges, L. Kaufman, A. Smola, and V. Vapnik, "Support vector regression machines," Advances in Neural Information Processing Systems, vol. 9, pp. 155-161, 1997.
37. J. A. Suykens and J. Vandewalle, "Least squares support vector machine classifiers," Neural Processing Letters, vol. 9, no. 3, pp. 293-300, 1999.