J4 ›› 2011, Vol. 49 ›› Issue (05): 906-910.

• 计算机科学 • 上一篇    下一篇

基于AP算法支持向量机的设计与应用

钟毅, 刘桂霞, 郑明, 沈威, 赖丽娜, 周春光   

  1. 吉林大学 计算机科学与技术学院, 长春 130012
  • 收稿日期:2010-12-21 出版日期:2011-09-26 发布日期:2011-09-27
  • 通讯作者: 刘桂霞 E-mail:liugx@jlu.edu.cn

Design and Application of Support Vector MachineBased on AP Algorithm

ZHONG Yi, LIU Guixia, ZHENG Ming, SHEN Wei, LAI Lina, ZHOU Chunguang   

  1. College of Computer Science and Technology, Jilin University, Changchun 130012, China
  • Received:2010-12-21 Online:2011-09-26 Published:2011-09-27
  • Contact: LIU Guixia E-mail:liugx@jlu.edu.cn

摘要:

设计一种基于AP聚类算法和SVM分类器相融合的新的混合分类器, 使用AP聚类算法优化数据集, 得到了高质量、 小样本的SVM分类器训练集. 实验结果表明: 与传统的SVM分类器相比, 混合分类器具有更高的分类精度; 在心脏病预测上, 该分类器的效果较好.

关键词: 支持向量机; AP聚类算法; 混合分类器; 心脏病预测

Abstract:

The authors proposed a new mixed classifier which is based on affinity propagation(AP) clustering algorithm and support vector machine(SVM). Using affinity propagation clustering algorithm which optimizes data set, we will get the highquality, small sample training data for SVM classifier, which resolves the problem of support vector machine classification inaccuracy. Experimental results show that the mixed model obtains higher classification accuracy and is better than traditional support vector machine. Especially applied to predicting heart disease, the proposed classifier obtains better results.

Key words: support vector machine, affinity propagation clustering algorithm, mixed classifier, prediction of heart disease

中图分类号: 

  • TP18