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Balanced Tree-Based Support Vector Machine for Friendly Analysis on Mobile Network

  • Han Wu
  • Bingqing Luo
  • Zhixin SunEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9771)

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.

Keywords

Support Vector Machine Friendly analysis on mobile network Classifier 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Key Laboratory of Broadband Wireless Communication and Sensor Network TechnologyNanjing University of Posts and TelecommunicationsNanjingChina

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