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http://dx.doi.org/10.3837/tiis.2022.04.002

Optimal SVM learning method based on adaptive sparse sampling and granularity shift factor  

Wen, Hui (New engineering industry college, Putian University)
Jia, Dongshun (Department of Liaohe Geophysical Prospecting, Bureau of Geophysical Prospecting INC)
Liu, Zhiqiang (New engineering industry college, Putian University)
Xu, Hang (New engineering industry college, Putian University)
Hao, Guangtao (New engineering industry college, Putian University)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.16, no.4, 2022 , pp. 1110-1127 More about this Journal
Abstract
To improve the training efficiency and generalization performance of a support vector machine (SVM) in a large-scale set, an optimal SVM learning method based on adaptive sparse sampling and the granularity shift factor is presented. The proposed method combines sampling optimization with learner optimization. First, an adaptive sparse sampling method based on the potential function density clustering is designed to adaptively obtain sparse sampling samples, which can achieve a reduction in the training sample set and effectively approximate the spatial structure distribution of the original sample set. A granularity shift factor method is then constructed to optimize the SVM decision hyperplane, which fully considers the neighborhood information of each granularity region in the sparse sampling set. Experiments on an artificial dataset and three benchmark datasets show that the proposed method can achieve a relatively higher training efficiency, as well as ensure a good generalization performance of the learner. Finally, the effectiveness of the proposed method is verified.
Keywords
Support vector machine; sparse sampling; granularity; granularity shift factor; large scale set;
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1 S Lu, Y Chen, X Zhu, et al., "Exploring Support Vector Machines for Big Data Analyses," in Proc. of 2021 4th International Conference on Computer Science and Software Engineering (CSSE 2021), Chengdu, China, pp.42-48, 2021.
2 Y Yan, Q Li, "An efficient augmented Lagrangian method for support vector machine," Optimization Methods and Software, vol. 35, no. 4, pp. 855-883, 2020.   DOI
3 G Guo, J Zhang, "Reducing examples to accelerate support vector regression," Pattern Recognition Letters, vol. 28, no. 16, pp. 2173-2183, 2007.   DOI
4 C. E. Shannon, "A Mathematical Theory of Communication," The Bell System Technical Journal, vol. 27, no. 3, pp. 379- 423, July. 1948.   DOI
5 C. CHANG, C. LIN, "LIBSVM: a library for support vector machines," 2016. [Online]. Available: https://www.csie.ntu.edu.tw/~cjlin/libsvm/
6 Z Liu, D. Elashoff, S. Piantadosi, "Sparse support vector machines with l0 approximation for ultra-high dimensional omics data," Artificial intelligence in medicine, vol. 96, pp. 134-141, 2019.   DOI
7 J. X. Bian, B. J. Ma, A. Paul, et al., "Research on electrochemical discharge machining based on image features and SVM algorithm," Journal of Intelligent & Fuzzy Systems, vol. 40, no.4, pp. 7247-7258, 2021.   DOI
8 J. Platt, Advances in Kernel Methods-Support Vector Learning, Cambridge, Mass., USA: MIT Press, 1998.
9 V. N. VAPNIK, "An overview of statistical learning theory," IEEE Transactions on neural networks, vol. 10, no. 5, pp. 988-999, Sept. 1999.   DOI
10 Q. H. Nguyen, B. P. Nguyen, T. B. Nguyen, et al., "Stacking segment-based CNN with SVM for recognition of atrial fibrillation from single-lead ECG recordings," Biomedical Signal Processing and Control, vol. 68, no.10, July. 2021.
11 J Ruan, X Wang, Y Shi, "Developing fast predictors for large-scale time series using fuzzy granular support vector machines," Applied Soft Computing Journal, vol. 13, no. 9, pp. 3981-4000, 2013.   DOI
12 Z You, J Yu, L Zhu, et al., "A MapReduce based parallel SVM for large-scale predicting protein interactions," Neurocomputing, vol. 145, pp. 37-43, Dec. 2014.   DOI
13 W Guo, N. K. Alham, Y Liu, et al., "A Resource Aware MapReduce Based Parallel SVM for Large Scale Image Classifications," Neural Processing Letters, vol. 44, no.1, pp.161-184, 2016.   DOI
14 J. Balczar, Y. Dai, O. Watanabe, "A random sampling technique for training support vector machines," in Proc. of the 12th International Conference on Algorithmic Learning Theory, Berlin, Germany: Springer-Verlag, pp. 119-134, 2001.
15 H Guo, W Wang, "Support vector machine based on hierarchical and dynamical granulation," Neurocomputing, vol. 211, pp. 22-33, 2016. .   DOI
16 Y Tang. B Jin, Y Zhang, "Granular support vector machines with association rules mining for protein homology prediction," Artificial Intelligence in Medicine, vol. 35, no.1-2, pp. 121-134, 2005.   DOI
17 X Lv, H Wang, X Zhang, et al., "An evolutional SVM method based on incremental algorithm and simulated indicator diagrams for fault diagnosis in sucker rod pumping systems," Journal of Petroleum Science and Engineering, vol. 203, 2021.
18 D Li, J Zhu, H Zhao, et al., "SVM-based online learning for interference-aware multi-cell mmWave vehicular communications," IET Communications, vol. 15, no. 8, pp. 1015-1027, March. 2021. .   DOI
19 Y Wang, X Zhang, S Wang, et al., "Nonlinear clustering-based support vector," Optimization Methods and Software, vol. 23, no. 4, pp. 533-549, 2008.   DOI
20 A. Mozafari, M. Jamzad, "Cluster-based adaptive SVM: A latent subdomains discovery method for domain adaptation problems," Computer Vision and Image Understanding, vol. 162, no. 1, pp. 116-134, SEP. 2017.   DOI
21 W Wang, H Guo, Y Jia, "Granular support vector machine based on mixed measure," Neurocomputing, vol. 101, pp. 116-128, 2013. .   DOI
22 Zhou S, "Sparse SVM for Sufficient Data Reduction," IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021.
23 S Ding, H Huang, J Yu, et al., "Research on the hybrid models of granular computing and support vector machine," Artifitial Intelligence Review, vol. 43, no. 6, pp. 565-577, 2015.   DOI
24 C. BLAKE, C. MERZ, "UCI repository of machine learning databases," 2018. [Online]. Available: http://archive.ics.uci.edu/ml/datasets.php
25 J Li, Y Tan, A Zhang, "The Application of Internet Big Data and Support Vector Machine in Risk Warning," in Proc. of Journal of Physics: Conference Series, vol. 1952, no. 4, pp. 1-11, 2021.
26 O. A. Bashkerov, E. M. Braverman, I.E. Muchnik, "Potential function algorithms for pattern recognition learning machines," Automatic Remote Control, vol. 25, no. 5, pp. 692-695, Jan. 1964.
27 S. HAYIN, Neural networks and learning machines, 3rd ed. Beijing: China Machine Press, 2009.
28 P. Konstantinos, T. Ioannis, D. George, et al., "A Support Vector Machine model for classification of efficiency: An application to M&A," Research in International Business and Finance, vol. 61, 2022.
29 J Yin, Q Li, "A semismooth Newton method for support vector classification and regression," Computational Optimization and Applications, vol. 73, no. 2, pp. 477-508, 2019.   DOI
30 P. D'Urso, J. M. Leski, "Fuzzy c-ordered medoids clustering for interval-valued data," Pattern Recognition, vol. 58, pp. 49-67, 2016. .   DOI
31 H Guo, W Wang, "Granular support vector machine: a review," Artifitial Intelligence Review, vol. 51, no. 1, pp. 19-32, 2019.   DOI
32 E. Osuna, R. Freund, F. Girosi, "Training support vector machines: an application to face detection," in Proc. of the IEEE conference on computer vision and pattern recognition, Los Alamitos, Puerto Rico: IEEE Computer Society, pp.130-136, 1997.