[1]HE Qiang,ZHANG Jiaoyang.Kernel-target alignment multi-kernel fuzzy support vector machine[J].CAAI Transactions on Intelligent Systems,2019,14(6):1163-1169.[doi:10.11992/tis.201904050]
Copy

Kernel-target alignment multi-kernel fuzzy support vector machine

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 14 Number of periods: 2019 6 Page number: 1163-1169 Column: 学术论文—机器学习 Public date: 2019-11-05
Title:
Kernel-target alignment multi-kernel fuzzy support vector machine
Author(s):
HE Qiang;  ZHANG Jiaoyang
School of Science, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
Keywords:
kernels; support vector machines; rough set theory; supervised learning; fuzzy classification; fuzzy set membership functions; robustness; noise
CLC:
TP181
DOI:
10.11992/tis.201904050
Abstract:
Support vector machines (SVMs) are widely used machine learning techniques. They are used to construct an optimal hyper-plane and have an extraordinary generalization capability and good performance. However, SVMs are sensitive to noise, and it is difficult to select an appropriate kernel for SVMs. In this paper, we introduce kernel-target alignment-based multi-kernel learning method into fuzzy support vector machine (FSVM) and propose the kernel-target alignment-based multi-kernel fuzzy support vector machine (MFSVM). First, we assign the corresponding membership degree to each sample point by the fuzzy rough set method, and then calculate the kernel weight by the multi-kernel learning based on the kernel alignment. Then, the combined kernel is introduced into the fuzzy SVM. The proposed method not only improves the anti-noise ability of the SVM but also effectively avoids the problem of kernel selection. Experiments on nine datasets of the UCI database show that the proposed method has a high classification accuracy, which verifies its feasibility and effectiveness.
References:
[1] VAPNIK V N. The nature of statistical learning theory[M]. 2nd ed. New York:Springer-Verlag, 1999:69-110.
[2] 邓乃扬, 田英杰. 支持向量机:理论、算法与拓展[M]. 北京:科学出版社, 2009:115-132.
[3] LIN Chunfu, WANG Shengde. Fuzzy support vector machines[J]. IEEE transactions on neural networks, 2002, 13(2):464-471.
[4] SIAHROUDI S K, MOODI P Z, BEIGY H. Detection of evolving concepts in non-stationary data streams:a multiple kernel learning approach[J]. Expert systems with applications, 2018, 91:187-197.
[5] GONEN M, ALPAYDIN E. Multiple kernel learning algorithms[J]. Journal of machine learning research, 2011, 12:2211-2268.
[6] CRISTIANINI N, SHAWE-TAYLOR J, ELISSEEFF A, et al. On kernel-target alignment[C]//Proceedings of the 14th International Conference on Neural Information Processing Systems:Natural and Synthetic. Vancouver, Canada, 2001:367-373.
[7] HE Qiang, WU Congxin. Membership evaluation and feature selection for fuzzy support vector machine based on fuzzy rough sets[J]. Soft computing, 2011, 15(6):1105-1114.
[8] LIN Chunfu, WANG Shengde. Training algorithms for fuzzy support vector machines with noisy data[J]. Pattern recognition letters, 2004, 25(14):1647-1656.
[9] CRISTIANINI N, SHAWE-TAYLOR J. An introduction to support vector machines and other kernel-based learning methods[M]. Cambridge:Cambridge University Press, 2000:1-28.
[10] DE DIEGO I M, MU?OZ A, MOGUERZA J M. Methods for the combination of kernel matrices within a support vector framework[J]. Machine learning, 2010, 78(1/2):137-174.
[11] CHRISTOUDIAS C M, URTASUN R, DARRELL T. Bayesian localized multiple kernel learning. Technical Report No. UCB/EECS-2009-96[R]. Berkeley:University of California, 2009:1531-1565.
[12] BEN-HUR A, NOBLE W S. Kernel methods for predicting protein-protein interactions[J]. Bioinformatics, 2005, 21(S1).
[13] QIU Shibin, LANE T. A Framework for multiple kernel support vector regression and its applications to siRNA efficacy prediction[J]. IEEE/ACM transactions on computational biology and bioinformatics, 2009, 6(2):190-199.
[14] VARMA M, RAY D. Learning the discriminative power-invariance trade-off[C]//Proceedings of 2007 IEEE 11th International Conference on Computer Vision. Rio de Janeiro, Brazil, 2007:1-8.
[15] 杜海洋. 简化多核支持向量机的研究[D]. 北京:北京交通大学, 2015:13-25. DU Haiyang. Research of reduced multiple kernel support vector machine[D]. Beijing:Beijing Jiaotong University, 2015:13-25.
[16] WANG Tinghua, ZHAO Dongyan, TIAN Shengfeng. An overview of kernel alignment and its applications[J]. Artificial intelligence review, 2015, 43(2):179-192.
[17] ZHONG Shangping, CHEN Daya, XU Qianfen, et al. Optimizing the Gaussian kernel function with the formulated kernel target alignment criterion for two-class pattern classification[J]. Pattern recognition, 2013, 46(7):2045-2054.
[18] 刘向东, 骆斌, 陈兆乾. 支持向量机最优模型选择的研究[J]. 计算机研究与发展, 2005, 42(4):576-581 LIU Xiangdong, LUO Bin, CHEN Zhaoqian. Optimal model selection for support vector machines[J]. Journal of computer research and development, 2005, 42(4):576-581
[19] 周炜, 周创明, 史朝辉, 等. 粗糙集理论及应用[M]. 北京:清华大学出版社, 2015:57-64.
[20] DUBOIS D, PRADE H. Rough fuzzy sets and fuzzy rough sets[J]. International journal of general systems, 1990, 17(2/3):191-209.
[21] DUBOIS D, PRADE H. Putting rough sets and fuzzy sets together[M]//S?OWI?SKI R. Intelligent Decision Support:Handbook of Applications and Advances of the Rough Sets Theory. Dordrecht, Netherlands:Springer, 1992:203-232.
[22] CHEN Degang, YANG Yongping, WANG Hui. Granular computing based on fuzzy similarity relations[J]. Soft computing, 2011, 15(6):1161-1172.
[23] CHEN Linlin, CHEN Degang, WANG Hui. Alignment based kernel selection for multi-label learning[J]. Neural processing letters, 2019, 49(3):1157-1177.
Similar References:

Memo

-

Last Update: 2019-12-25

Copyright © CAAI Transactions on Intelligent Systems