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JACIII Vol.21 No.5 pp. 795-802
doi: 10.20965/jaciii.2017.p0795
(2017)

Paper:

Reproducing Polynomial Kernel Extreme Learning Machine

Yibo Li*, Chao Liu*, Senyue Zhang**, Wenan Tan**, and Yanyan Ding*

*School of Automation, Shenyang Aerospace University
Shenyang, Liaoning 110136, China

**College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics
Nanjing, Jiangsu 211106, China

Received:
April 27, 2017
Accepted:
June 19, 2017
Published:
September 20, 2017
Keywords:
kernel extreme learning machine, combined kernel function, reproducing kernel function, cuckoo search algorithm
Abstract

Conventional kernel support vector machine (KSVM) has the problem of slow training speed, and single kernel extreme learning machine (KELM) also has some performance limitations, for which this paper proposes a new combined KELM model that build by the polynomial kernel and reproducing kernel on Sobolev Hilbert space. This model combines the advantages of global and local kernel function and has fast training speed. At the same time, an efficient optimization algorithm called cuckoo search algorithm is adopted to avoid blindness and inaccuracy in parameter selection. Experiments were performed on bi-spiral benchmark dataset, Banana dataset, as well as a number of classification and regression datasets from the UCI benchmark repository illustrate the feasibility of the proposed model. It achieves the better robustness and generalization performance when compared to other conventional KELM and KSVM, which demonstrates its effectiveness and usefulness.

References
  1. [1] G. B. Huang, X. Ding, and H. Zhou, “Optimization method based extreme learning machine for classification,” Neurocomputing, Vol.74, pp. 155-163, 2010.
  2. [2] G. B. Huang, Q. Y. Zhu, and C. K. Siew, “Extreme learning machine: a new learning scheme of feed forward neural networks,” Proc. of Int. Joint Conf. on Neural Networks (IJCNN2004), pp. 985-990, 2004.
  3. [3] G. B. Huang and H. Zhou, “Extreme learning machine for regression and multiclass classification,” IEEE Trans. on Systems, Man, and Cybernetics – Part B: Cybernetics, Vol.42, No.2, pp. 513-529, 2012.
  4. [4] L. Zhang and D. Zhang, “Evolutionary Cost-Sensitive Extreme Learning Machine,” IEEE Trans. on Neural Networks & Learning Systems, Vol.PP, Issue 99, pp. 1-16, 2016.
  5. [5] C. D. Li, Y. S. Lv, J. Q. Yi, and G. Q. Zhang, “Pruned Fast Learning Fuzzy Approach for Data-Driven Traffic Flow Prediction,” J. Adv. Comput. Intell. Intell. Inform, Vol.20, No.7, pp. 1181-1191, 2016.
  6. [6] L. Zhang and D. Zhang, “Robust Visual Knowledge Transfer via Extreme Learning Machine Based Domain Adaptation,” IEEE Trans. on Image Processing A Publication of the IEEE Signal Processing Society, Vol.25, Issue 10, pp 4959-4973, 2016.
  7. [7] X. L. Tang and M. Han, “Ternary reversible extreme learning machines: the incremental tri-training method for semi-supervised classification,” Knowl Inf Syst, Vol.22, No.3, pp. 345-372, 2010.
  8. [8] L. Zhang and D. Zhang, “Domain Adaptation Extreme Learning Machines for Drift Compensation in E-Nose Systems,” IEEE Trans. on Instrumentation & Measurement, Vol.64, No.7, pp. 1790-1801, 2015.
  9. [9] L. Zhang and P. Deng, “Abnormal Odor Detection in Electronic Nose via Self-Expression Inspired Extreme Learning Machine,” IEEE Trans. on Systems Man & Cybernetics Systems, Vol.PP, Issue 99, pp. 1-11, 2017.
  10. [10] M. A. Chao, Y. T. Zhang, and L. I. Zhi-Ning, “Engine Characteristic Parameters Prediction based on PSO-KELM,” Control Engineering of China, Vol.50, No.8, pp. 1-5, 2014.
  11. [11] H. Lu, B. Du, J. Liu, et al., “A kernel extreme learning machine algorithm based on improved particle swam optimization,” Memetic Computing, pp. 1-8, 2016.
  12. [12] X. U. Lixiang, L. I. Xu, L. V. Wanli, et al., “New model analysis method of combined kernel Support Vector Machine,” Computer Engineering & Applications, 2013.
  13. [13] F. Mokhtari and T. Mourid, “Prediction of Continuous Time Autoregressive Processes via, the Reproducing Kernel Spaces,” Statistical Inference for Stochastic Processes, Vol.6, No.3, pp. 247-266, 2003.
  14. [14] S. Ikeda and Y. Sato, “Kernel Canonical Discriminant Analysis Based on Variable Selection,” J. Adv. Comput. Intell. Intell. Inform, Vol.13, No.4, pp. 416-420, 2009.
  15. [15] G. B. Huang and L. Chen, “Convex Incremental Extreme Learning Machine,” Neurocomputing, Vol.70, No.1, pp. 7056-3062, 2007.
  16. [16] G. B. Huang and L. Chen, “Enhanced Random Search Based Incremental Extreme Learning Machine,” Neurocomputing, Vol.71, No.1, pp. 3460-3468, 2008.
  17. [17] G. F. Smits and E. M. Jordaan, “Improved SVM regression using mixtures of kernels[C],” Int. Joint Conf. on Neural Networks, IEEE Xplore, pp. 2785-2790, 2002.
  18. [18] B. Christopher, “Pattern Recognition and Machine Learning,” New York: Springer-Verlag, 2007.
  19. [19] G. S. Wang, “Properties and construction methods,” Computer Science, Vol.6, pp. 172-174+178, 2006.
  20. [20] S. Saitoh, “Inequalities in the most simple Sobolev space and convolutions of L2functions with weights,” Proc. of the American Mathematical Society, Vol.118, No.2, pp. 515-515, 1993.
  21. [21] C. Lian, Z. Zeng, W. Yao, et al., “Displacement prediction of landslide based on PSOGSA-ELM with mixed kernel,” 6th Int. Conf. on Advanced Computational Intelligence. IEEE, pp. 52-57, 2013.
  22. [22] J. Xie, “Time Series Prediction Based on Recurrent LS-SVM with Mixed Kernel,” Asia-Pacific Conf. on Information Processing, IEEE Computer Society, pp. 113-116, 2009.
  23. [23] X. S. Yang and S. Deb, “Engineering optimization by cuckoo search,” Int. J. of Mathematical Modeling and Numerical Optimization, Vol.11, No.4, pp. 330-343, 2012.
  24. [24] L. Ma, J. Zhao, J. Wang, et al., “Fault diagnosis of hydraulic system of quadruped robot by SVM based on rough set and CS algorithm,” Control Conf. IEEE, pp. 6264-6268, 2015.

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Last updated on Dec. 12, 2017