计算机科学 ›› 2018, Vol. 45 ›› Issue (5): 220-223.doi: 10.11896/j.issn.1002-137X.2018.05.037
冷强奎,刘福德,秦玉平
LENG Qiang-kui, LIU Fu-de and QIN Yu-ping
摘要： 为提高多类支持向量机的分类效率,提出了一种基于混合二叉树结构的多类支持向量机分类算法。该混合二叉树中的每个内部结点对应一个分割超平面,该超平面通过计算两个距离最远的类的质心而获得,即该超平面为连接两质心线段的垂直平分线。每个终端结点(即决策结点)对应一个支持向量机,它的训练集不再是质心而是两类(组)样本集。该分类模型通常是超平面和支持向量机的混合结构,其中超平面实现训练早期的近似划分,以提升分类速度；而支持向量机完成最终的精确分类,以保证分类精度。实验结果表明,相比于经典的多类支持向量机方法,该算法在保证分类精度的前提下,能够有效缩短计算时间,提升分类效率。
| [1] VAPNIK V.The nature of statistical learning theory[M].New York:Springer-Verlag,1995. [2] VAPNIK V.Statistical learning theory[M].New York:Wiley-Interscience,1998. [3] HSU C W,LIN C J.A comparison of methods for multiclass support vector machines[J].IEEE Transactions on Neural Networks,2002,13(2):415-425. [4] ROKACH L.Ensemble-based classifiers[J].Artificial Intelli-gence Review,2010,33(1/2):1-39. [5] KREβEL U H G.Pairwise classification and support vector machines[M]∥Advances in Kernel Methods.MIT Press,1999:255-268. [6] LORENA A C,DE CARVALHO A C,GAMA J M P.A review on the combination of binary classifiers in multiclass problems[J].Artificial Intelligence Review,2008,30(1):19-37. [7] PLATT J C,CRISTIANINI N,SHAWE-TAYLOR J.Largemargin DAGs for multiclass classification[C]∥12th International Conference on Neural Information Processing Systems,MIT Press.1999:547-553. [8] KIJSIRIKUL B,USSIVAKUL N.Multiclass support vector machines using adaptive directed acyclic graph[C]∥2002 International Joint Conference on Neural Networks.IEEE,2002:980-985. [9] BENNETT K P,BLUE J A.A support vector machine approach to decision trees[C]∥1998 IEEE International Joint Conference on Neural Networks.IEEE,1998:2396-2401. [10] FEI B,LIU J.Binary tree of SVM:a new fast multiclass training and classification algorithm[J].IEEE Transactions on Neural Networks,2006,17(3):696-704. [11] CHEONG S,SANG H,LEE S Y.Support vector machines with binary tree architecture for multi-class classification[J].Neural Information Processing Letters and Reviews,2004,2(3):47-51. [12] KANG S,CHO S,KANG P.Multi-class classification via hetero-geneous ensemble of one-class classifiers[J].Engineering Applications of Artificial Intelligence,2015,43(C):35-43. [13] TOMAR D,AGARWAL S.A comparison on multi-class classification methods based on least squares twin support vector machine[J].Knowledge-Based Systems,2015,81(C):131-147. [14] YANG Z,WU H,LI C,et al.Least squares recursive projection twin support vector machine for multi-class classification[J].International Journal of Machine Learning and Cybernetics,2016,7(3):411-426. [15] LIU M,ZHANG D,CHEN S,et al.Joint binary classifier lear-ning for ecoc-based multi-class classification[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2016,38(11):2335-2341. [16] SONG Q,XIAO X,JIANG H,et al.A new multi-class classification method based on minimum enclosing balls[J].Journal of Mechanical Science and Technology,2015,29(8):3467-3473. [17] KOSTIN A.A simple and fast multi-class piecewise linear pattern classifier[J].Pattern Recognition,2006,39(11):1949-1962. [18] ARONSZAJN N.Theory of reproducing kernels[J].Transactions of the American Mathematical Society,1950,68(3):337-404. [19] SNYDER W E,TANG D A.Finding the extrema of a region[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1980,2(3):266-269. [20] BRAZDIL P,GAMA J.Statlog datasets [OL/DB].[2016-10-25].http://www.liacc.up.pt/ml/old/statlog/datasets.html. [21] FRANK A,ASUNCION A.UCI machine learning repository [OL/DB].[2016-10-20].http://archive.ics.uci.edu/ml. [22] CHANG C C,LIN C J.Libsvm:a library for support vector machines .[2016-10-26].http://www.csie.ntu.edu.tw/cjlin/libsvm. [23] HSU C W,LIN C J.A comparison of methods for multiclasssupport vector machines[J].IEEE Transactions on Neural Networks,2002,13(2):415-425. |
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