Computer Engineering and Applications ›› 2010, Vol. 46 ›› Issue (24): 135-137.DOI: 10.3778/j.issn.1002-8331.2010.24.041

• 数据库、信号与信息处理 • Previous Articles     Next Articles

Classification algorithm research based on support vector machine and reverse K-nearest neighbor

CHEN Li1,CHEN Jing1,GAO Xin-tao2,WANG Lai-sheng1   

  1. 1.College of Sciences,China Agricultural University,Beijing 100083,China
    2.Department of Mathematics,Zhengzhou University,Zhengzhou 450001,China
  • Received:2009-02-06 Revised:2009-04-02 Online:2010-08-21 Published:2010-08-21
  • Contact: CHEN Li

基于支持向量机与反K近邻的分类算法研究

陈 丽1,陈 静1,高新涛2,王来生1   

  1. 1.中国农业大学 理学院,北京 100083
    2.郑州大学 数学系,郑州 450001
  • 通讯作者: 陈 丽

Abstract: When Support Vector Machine(SVM) is used to solve the classification problems,the samples nearby the SVM hyperplanes are more easily misclassified.To solve this problem,the Reverse K-Nearest Neighbor method is introduced into the classification problems,and the Reverse K-Nearest Neighbor classification method(RKNN) is presented.And then,a new classification algorithm based on Support Vector Machine and Reverse K-Nearest Neighbor classification method(SVM-RKNN) is presented.At last,in order to avoid the one-sidedness problems which may be produced by one single classifier,the multi-fusion method based on SVM-RKNN is presented.The experimental results show that the average forecast accuracy of the SVM-RKNN method increases 2.13% than the SVM method,and the average forecast accuracy of the multi-fusion method based on SVM-RKNN increases 2.54% and 0.41% than the SVM and SVM-RKNN method respectively.

摘要: 针对支持向量机在对样本进行分类时,决策超平面附近的点较易错分的问题,首先将反K近邻法引入分类问题,提出了反K近邻分类算法;然后,将支持向量机(SVM)与反K近邻分类算法(RKNN)相结合,提出了基于支持向量机与反K近邻的分类算法(SVM-RKNN);最后,为了避免单一分类器可能存在的片面性问题,提出了基于SVM-RKNN的多特征融合分类方法。实验结果表明:SVM-RKNN分类算法的分类准确率比SVM方法平均提高了2.13%,而基于SVM-RKNN的多特征融合分类算法的分类准确率分别比SVM和SVM-RKNN算法平均提高了2.54%和0.41%。

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