JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE) ›› 2015, Vol. 45 ›› Issue (1): 13-18.doi: 10.6040/j.issn.1672-3961.1.2014.072

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An algorithm of fast local support vector machine based on clustering

HAO Qingbo1, MU Shaomin1,2, YIN Chuanhuan3, CHANG Tengteng1, CUI Wenbin1   

  1. 1. School of Information Science and Engineering, Shandong Agricultural University, Taian 271018, Shandong, China;
    2. Agricultural Big-Data Research Center, Shandong Agricultural University, Taian 271018, Shandong, China;
    3. School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China
  • Received:2014-03-26 Revised:2015-01-08 Online:2015-02-20 Published:2014-03-26

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Abstract: In order to further improve the classification efficiency and precision of local support vector machine, a new algorithm was proposed.The two major improvements were as follows. First, every type of training samples was clustered seperately, and the training samples were substituted for sample centers generated by clustering. Second, the k nearest neighbors of test samples were selected by using the improved k-nearest neighbor algorithm. Tests were done on UCI data sets and bark image data sets made by the proposed algorithm to verify its effectiveness. Experimental results demonstrated that this algorithm had certain superiority of classification accuracy and efficiency.

Key words: kernel function, local support vector machine, texture features, k-nearest neighbor, k-means clustering, classification

CLC Number: 

  • TP391
[1] VAPNIK V. The nature of statistical learning theory[M]. Berlin, Heidelberg: Springer, 2000:267-287.
[2] CORTES C, VAPNIK V. Support-vector networks[J]. Machine Learning, 1995, 20(3):273-297.
[3] BURGES C J C. A tutorial on support vector machines for pattern recognition[J]. Data Mining and Knowledge Discovery, 1998, 2(2):121-167.
[4] SMOLA A J, SCHLKOPF B. A tutorial on support vector regression[J]. Statistics and Computing, 2004, 14(3):199-222.
[5] 邓乃扬, 田英杰. 数据挖掘中的新方法:支持向量机[M]. 北京: 科学出版社, 2004:164-185.
[6] 牟少敏. 核方法的研究及其应用[D]. 北京: 北京交通大学计算机与信息技术学院, 2008:17-21. MU Shaomin. Research on kernels method and application[D]. Beijing: School of Computer and Information Technology, Beijing Jiaotong University, 2008:17-21.
[7] 饶鲜, 董春曦, 杨绍全. 基于支持向量机的入侵检测系统[J]. 软件学报, 2003, 14(4):798-803. RAO Xian, DONG Chunxi, YANG Shaoquan. An intrusion detection system based on support vector machine[J]. Journal of Software, 2003, 14(4):798-803.
[8] BLANZIERI E, MELGANI F. Nearest neighbor classification of remote sensing images with the maximal margin principle[J]. IEEE Transactions on Geoscience and Remote Sensing, 2008, 46(6):1804-1811.
[9] 尹传环. 结构化数据核函数的研究[D]. 北京: 北京交通大学计算机与信息技术学院, 2008:3-9. YIN Chuanhuan. Research on kernels for structured data[D]. Beijing: School of Computer and Information Technology, Beijing Jiaotong University, 2008:3-9.
[10] LIU Q, TANG X, LU H, et al. Face recognition using kernel scatter-difference-based discriminant analysis[J]. IEEE Transactions on Neural Networks, 2006, 17(4):1081-1085.
[11] WANG X, CHUNG F, WANG S. On minimum class locality preserving variance support vector machine[J]. Pattern Recognition, 2010, 43(8):2753-2762.
[12] WANG H, CHEN S, HU Z, et al. Locality-preserved maximum information projection[J]. IEEE Transactions on Neural Networks, 2008, 19(4):571-585.
[13] ZHANG T. Statistical behavior and consistency of classification methods based on convex risk minimization[J]. Annals of Statistics, 2004, 32(1):56-85.
[14] STEINWART I. Support vector machines are universally consistent[J]. Journal of Complexity, 2002, 18(3):768-791.
[15] 顾彬, 郑关胜, 王建东. 增量和减量式标准支持向量机的分析[J]. 软件学报, 2013, 24(7):1601-1613. GU Bin, ZHENG Guansheng, WANG Jiandong. Analysis for incremental and decremental standard support vector machine[J]. Journal of Software, 2013, 24(7):1601-1613.
[16] BRAILOVSKY V L, BARZILAY O, SHAHAVE R. On global, local, mixed and neighborhood kernels for support vector machines[J]. Pattern Recognition Letters, 1999, 20(11):1183-1190.
[17] ZAKAI A, RITOV Y. Consistency and localizability[J]. The Journal of Machine Learning Research, 2009, 10(4):827-856.
[18] 尹传环, 牟少敏, 田盛丰, 等. 局部支持向量机的研究进展[J]. 计算机科学, 2012, 39(1):170-174. YIN Chuanhuan, MU Shaomin, TIAN Shengfeng, et al. Survey of recent trends in local support vector machine[J]. Computer Science, 2012, 39(1):170-174.
[19] SEGATA N, BLANZIERI E. Fast and scalable local kernel machines[J]. The Journal of Machine Learning Research, 2010, 11(6):1883-1926.
[20] SHEN M, CHEN J, LIN C. Modeling of nonlinear medical signal based on local support vector machine[C]//Instrumentation and Measurement Technology Conference. Singapore: IEEE, 2009:675-679.
[21] YANG X, CHEN S, CHEN B, et al. Proximal support vector machine using local information[J]. Neurocomputing, 2009, 73(1):357-365.
[22] CHENG H, TAN P, JIN R. Efficient algorithm for localized support vector machine[J]. IEEE Transactions on Knowledge and Data Engineering, 2010, 22(4): 537-549.
[23] KHEMCHANDANI R, CHANDRA S. Twin support vector machines for pattern classification[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(5):905-910.
[24] ALIFERIS C F, TSAMARDINOS I, STATNIKOV A R, et al. Causal explorer: a causal probabilistic network learning toolkit for biomedical discovery[C]//Proceedings of the International Conference on Mathematics and Engineering Techniques in Medicine and Biological Sciences. Las Vegas, USA: METMBS, 2003:371-376.
[25] ZHANG H, BERG A C, MAIRE M, et al. SVM-kNN:discriminative nearest neighbor classification for visual category recognition[C]//IEEE Computer Society Conference on Computer Vision and Pattern Recognition. New York, USA: IEEE, 2006:2126-2136.
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