• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

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

Vol. 16, No. 4, April 30, 2022
10.3837/tiis.2022.04.002, Download Paper (Free):

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.


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Cite this article

[IEEE Style]
H. Wen, D. Jia, Z. Liu, H. Xu and G. Hao, "Optimal SVM learning method based on adaptive sparse sampling and granularity shift factor," KSII Transactions on Internet and Information Systems, vol. 16, no. 4, pp. 1110-1127, 2022. DOI: 10.3837/tiis.2022.04.002.

[ACM Style]
Hui Wen, Dongshun Jia, Zhiqiang Liu, Hang Xu, and Guangtao Hao. 2022. Optimal SVM learning method based on adaptive sparse sampling and granularity shift factor. KSII Transactions on Internet and Information Systems, 16, 4, (2022), 1110-1127. DOI: 10.3837/tiis.2022.04.002.

[BibTeX Style]
@article{tiis:25580, title="Optimal SVM learning method based on adaptive sparse sampling and granularity shift factor", author="Hui Wen and Dongshun Jia and Zhiqiang Liu and Hang Xu and Guangtao Hao and ", journal="KSII Transactions on Internet and Information Systems", DOI={10.3837/tiis.2022.04.002}, volume={16}, number={4}, year="2022", month={April}, pages={1110-1127}}