UNSUPERVISED FEATURE SELECTION BASED ON FEATURE RELEVANCE

被引：3
|
作者：
Zhang, Feng [1 ]
Zhao, Ya-Jun [2 ]
Chen, Jun-Fen [1 ]
机构：
[1] Hebei Univ, Coll Math & Comp Sci, Key Lab Machine Learning & Computat Intelligence, Baoding 071002, Peoples R China
[2] Hebei Univ, Coll Phys Sci & Technol, Baoding 071002, Peoples R China
来源：
PROCEEDINGS OF 2009 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-6 | 2009年
关键词：
Unsupervised learning; Feature selection; Mutual information; Clustering; MUTUAL INFORMATION;
D O I：
10.1109/ICMLC.2009.5212453
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
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.
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页码：487 / +
页数：2
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