计算机工程与应用 ›› 2009, Vol. 45 ›› Issue (23): 49-51.DOI: 10.3778/j.issn.1002-8331.2009.23.014

• 研究、探讨 • 上一篇    下一篇

支持向量机的一种特征选取算法

陈启买,陈森平   

  1. 华南师范大学 计算机学院,广州 510631
  • 收稿日期:2009-02-02 修回日期:2009-04-15 出版日期:2009-08-11 发布日期:2009-08-11
  • 通讯作者: 陈启买

Feature selection algorithm in support vector machine

CHEN Qi-mai,CHEN Sen-ping   

  1. School of Computer Science,South China Normal University,Guangzhou 510631,China
  • Received:2009-02-02 Revised:2009-04-15 Online:2009-08-11 Published:2009-08-11
  • Contact: CHEN Qi-mai

摘要: 支持向量机(Support Vector Machine,SVM)是一种有效的分类方法,其学习本质是通过对偶问题求解原问题,但是它不能直接获得特征重要性。提出一种新的特征选取算法,实验表明,该特征选取算法与一般特征选取算法(如F-Score算法)相比,对同一测试数据集计算的结果具有相同的降序排列结果,而且有更好的特征刻画量化指标,分界线更明显,表明新的特征选取算法具有更佳的合理性。

关键词: 支持向量机, 特征选取, F-Score

Abstract: SVM is an effective classification method,the nature of learning is that it solves the original problem by the dual problem,but it does not directly obtain the feature importance.This article raises a new feature selection algorithm.Experiments show that the new feature selection algorithm and F-Score algorithm for calculating the same test data set is the result of the same descending order.The new feature selection algorithm is reasonable.

Key words: Support Vector Machine(SVM), feature selection, F-Score

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