UNSUPERVISED FEATURE SELECTION BASED ON FEATURE RELEVANCE

摘要：

Feature selection is an essential technique used in data mining and machine learning. Many feature selection methods have been studied for supervised problems. However feature selection for unsupervised learning is rarely studied. In this paper, we proposed an approach to select features for unsupervised problems. Firstly, the original features are clustered according to their relevance degree defined by mutual information. And then the most informative feature is selected from each cluster based on the contribution-information of each feature. The experimental results show that the proposed method can match some popular supervised feature selection methods. And the features selected by our method do include most of the information hidden in the overall original features.

关键词： Unsupervised learning Feature selection Mutual information Clustering

作者: FENG ZHANG YA-JUN ZHAO JUN-FEN CHEN

作者单位: Key Lab.of Machine Learning and computational Intelligence, College of Mathematics and Computer Scie College of Physics Science and Technology, Hebei University, Baoding, 071002, China

会议类型: 国际会议

会议名称: 2009 International Conference on Machine Learning and Cybernetics(2009机器学习与控制论国际会议)

会议地点: 保定

会议语种:英文

页码: 487-492

在线出版日期: 2009-07-12（万方平台首次上网日期，不代表论文的发表时间）