Abstract
In this paper, a balanced tree-based Support Vector Machine is proposed. Based on the traditional Support Vector Machine algorithm, Support Vector Machine classifier is trained by comparing the dissimilarity matrix. The samples which have minimum dissimilarity will be trained first, and the less their dissimilarities are, the earlier they will be trained. These samples are merged in accordance with the training sequence and generate a binary balanced tree gradually. The classification algorithm is used in the friendly analysis on the mobile network, which makes the analysis more convinced. The new proposed SVM algorithm analyze on different kinds of basic energy consumption data collected by the mobile terminal and use these data to classify the applications, and after the related judge process, the mobile application will get the friendly analysis results. Experimental results show that the proposed balanced tree-based SVM algorithm can realize the classification and friendly analysis more accurately.
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Wu, H., Luo, B., Sun, Z. (2016). Balanced Tree-Based Support Vector Machine for Friendly Analysis on Mobile Network. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Application. ICIC 2016. Lecture Notes in Computer Science(), vol 9771. Springer, Cham. https://doi.org/10.1007/978-3-319-42291-6_18
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DOI: https://doi.org/10.1007/978-3-319-42291-6_18
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