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)


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.


Support Vector Machine Friendly analysis on mobile network Classifier 


  1. 1.
    Zhang, Q., Luo, M., Xue, Y., Tan, J.: Multi-class text categorization based on immune algorithm. In: International Workshop on Education Technology and Training, 2008, and 2008 International Workshop on Geoscience and Remote Sensing, ETT and GRS 2008, vol. 1, pp. 749–752, 21–22 December 2008Google Scholar
  2. 2.
    Ye, Q., Liang, J., Jiao, J.: Pedestrian detection in video images via error correcting output code classification of manifold subclasses. IEEE Trans. Intell. Transp. Syst. 13(1), 193–202 (2012)CrossRefGoogle Scholar
  3. 3.
    Xiao-feng, L., Xue-ying, Z., Ji-kang, D.: Speech recognition based on support vector machine and error correcting output codes. In: 2010 First International Conference on Pervasive Computing Signal Processing and Applications (PCSPA), pp. 336–339, 17–19 September 2010Google Scholar
  4. 4.
    Jian, B., Liu, R.: M-ary support vector machine (M-ary SVM) for multi-category classification. J. Comput. Appl. 03, 661–664 (2012)Google Scholar
  5. 5.
    Duan, T., Zhang, D.-N.: Application of multiclass SVM based on binary tree in targeting grouping. Radio Eng. 06, 88–91 (2015)MathSciNetGoogle Scholar
  6. 6.
    Juan-Ying, X., Bing-Quan, Z., Wan-Zi, W.: A partial binary tree algorithm for multiclass classification based on twin support vector machines. J. Nanjing Univ. 47(4), 354–363 (2011)MathSciNetGoogle Scholar
  7. 7.
    Xue, S., Jing, X., Sun, S., Huang, H.: Binary-decision-tree-based multiclass Support Vector Machines. In: International Symposium on Communications and Information Technologies, pp. 85–89 (2014)Google Scholar
  8. 8.
    Guan, T., Frey, C.W.: Using ensemble of decision trees with SVM nodes to learn the behaviour of a transmission control software. In: 2014 IEEE 17th International Conference on Intelligent Transportation Systems (ITSC), pp. 1323–1328, 8–11 October 2014Google Scholar
  9. 9.
    Li, J.J., Wang, Y., Liu, R.Q.: Selection of materialized view based on information weight and using Huffman-tree on spatial data warehouse. In: First International Conference on Innovative Computing, Information and Control, ICICIC 2006, vol. 2, pp. 71–74, 30 August 2006–1 September 2006Google Scholar
  10. 10.
    Xu, Q., Zhang, X.: Multiclass feature selection algorithms base on R-SVM. In: 2014 IEEE China Summit and International Conference on Signal and Information Processing (ChinaSIP), pp. 525–529, 9–13 July 2012Google Scholar
  11. 11.
    Freeman, C., Kulic, D., Basir, O.: Feature-selected tree-based classification. IEEE Trans. Cybern. 43(6), 1990–2004 (2013)CrossRefGoogle Scholar
  12. 12.
    Yuncu, E., Hacihabiboglu, H., Bozsahin, C.: Automatic speech emotion recognition using auditory models with binary decision tree and SVM. In: 2014 22nd International Conference on Pattern Recognition (ICPR), pp. 773–778, 24–28 August 2014Google Scholar
  13. 13.
    Gu, Y., Zhao, W., Wu, Z.: Least squares support vector machine algorithm. J. Tsinghua Univ. (Sci. Technol.) 07, 1063–1066+1071 (2010)Google Scholar
  14. 14.
    Wang, C., Chen, S.: An improved LS-SVM based on SSOR-PCG. In: 2013 Ninth International Conference on Natural Computation (ICNC), pp. 28–33, 23–25 July 2013Google Scholar
  15. 15.
    Insom, P., Cao, C., Boonsrimuang, P., Liu, D., Saokarn, A., Yomwan, P., Xu, Y.: A support vector machine-based particle filter method for improved flooding classification. IEEE Geosci. Remote Sens. Lett. PP(99), 1–5 (2015)Google Scholar
  16. 16.
    Zeng, L.-M., Wu, X.-B., Liu, P.: Sample reduction strategy for SVM large scale training data set using PSO. Comput. Sci. 09, 215–217 (2009)Google Scholar

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

Personalised recommendations