冷强奎,刘福德,秦玉平.一种基于混合二叉树结构的多类支持向量机分类算法[J].计算机科学,2018,45(5):220-223, 237
一种基于混合二叉树结构的多类支持向量机分类算法
Multi-class Classification Algorithm for SVM Based on Hybrid Binary Tree Structure
投稿时间:2017-05-18  修订日期:2017-07-05
DOI:10.11896/j.issn.1002-137X.2018.05.037
中文关键词:  支持向量机,多类分类,混合二叉树,质心表达
英文关键词:SVM,Multi-class classification,Hybrid binary tree,Centroid representation
基金项目:本文受国家自然科学基金项目(61602056),辽宁省博士科研启动基金项目(201601348),辽宁省教育厅科研项目(LZ2016005)资助
作者单位E-mail
冷强奎 渤海大学信息科学与技术学院 辽宁 锦州121000 qkleng@gmail.com 
刘福德 渤海大学大学基础教研部 辽宁 锦州121000 fdliu@gmail.com 
秦玉平 渤海大学工学院 辽宁 锦州121000 jzqinyuping@gmail.com 
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中文摘要:
      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。
英文摘要:
      In order to improve the classification efficiency of mutli-class support vector mechine,a multi-class classification algorithm for support vector machine(SVM) based on hybrid binary tree structure was proposed.In the structure,each internal node corresponds to a partition hyperplane,which is obtained as perpendicular bisectors of linking two centroid segements of the two farthest classes from each other.Each terminal node(i.e.,decision node) is associated with a SVM,whose training set is two sets of samples instead of two centroids.In general,the resulting classification model represents a hybrid form,consisting of hyperplanes and SVMs.The approximate hyperplanes by centroids can provide fast partition in the early stages of the training phase,whereas the SVMs will perform the final precise decision.Experimental results show that compared with the classical multi-class SVM,the proposed algorithm can reduce the computational time and improve the classification efficiency with similar classification accuracy.
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