Abstract
The parameters selection and optimization of kernel function is the core of support vector machine (SVM), which is closely related to the distribution of datasets. It can be obtained a series of different group index, either mapped by different kernel functions on the same dataset or did by same kernel function on the different subsets. We analyze the impact of the choice of kernel functions and parameters on the performance of SVM, and propose a GA-optimized weighted mixed kernel function of SVM based on information entropy (GA-IE-RBF-SVM). The algorithm uses the information entropy to improve the contribution of the features that are conducive to classification firstly to mitigate falling into a local optimum, then learn from the idea of multi-core learning to enhance the adaptability of the algorithm. The optimal genetic algorithm (GA) is used to select the type of mixed kernel function, kernel function parameters and error penalty factor. The experimental results show that compared with other similar algorithms, this algorithm has a higher classification accuracy rate and faster convergence speed.
This work is supported by the National Natural Science Foundation of China (No. 61373135, No. 61672299, No. 61702281, No. 61602259), by the Natural Science Foundation of Jiangsu Province (No. BK20150866, No. BK20140883, No. BK20160913), and by the Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX17_0773).
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Zhao, X., Zhang, X., Xiang, F., Ye, F., Sun, Z. (2019). A GA-Optimized Weighted Mixed Kernel Function of SVM Based on Information Entropy. In: Sun, X., Pan, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2019. Lecture Notes in Computer Science(), vol 11633. Springer, Cham. https://doi.org/10.1007/978-3-030-24265-7_34
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DOI: https://doi.org/10.1007/978-3-030-24265-7_34
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