Computer Engineering and Applications ›› 2010, Vol. 46 ›› Issue (8): 130-132.DOI: 10.3778/j.issn.1002-8331.2010.08.037

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

Spatial overlapping based semi-supervised feature selection

CHEN Hong1,2,GUO Gong-de1,2   

  1. 1.School of Mathematics and Computer Science,Fujian Normal University,Fuzhou 350007,China
    2.Key Laboratory of Network Security and Cryptography,Fujian Normal University,Fuzhou 350007,China
  • Received:2008-10-10 Revised:2008-12-18 Online:2010-03-11 Published:2010-03-11
  • Contact: CHEN Hong


陈 红1,2,郭躬德1,2   

  1. 1.福建师范大学 数学与计算机科学学院,福州 350007
    2.福建师范大学 网络安全与密码技术重点实验室,福州 350007
  • 通讯作者: 陈 红

Abstract: A novel Spatial Overlapping based Semi-supervised Feature Selection(SOS-FS) method is proposed.It uses both labeled and unlabeled data in feature selection,feature’s relevance is measured by its overlapping ratio among different clusters.Experimental results carried out on some public datasets collected from the UCI machine learning repository and predictive toxicology domain show that SOS-FS has a promising performance on the improvement of the learning accuracy.

摘要: 提出一种新颖的基于空间覆盖的半监督特征选择方法。该算法同时利用已标签数据与未标签数据进行特征选择,各特征的相关性大小由其在不同簇中的覆盖程度衡量。在公共数据集和毒性数据集上的实验表明,该方法在改善学习精度上有很好的应用前景。

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