An Experimental Study on Unsupervised Clustering-based Feature Selection Methods

被引：1

作者：

Covoes, Thiago F. ^{[1
]}

Hruschka, Eduardo R. ^{[1
]}

机构：

[1] Univ Sao Paulo, Dept Comp Sci, Sao Carlos, SP, Brazil

来源：

2009 9TH INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS DESIGN AND APPLICATIONS | 2009年

关键词：

unsupervised feature selection; feature clustering; clustering problems; GENE-EXPRESSION DATA; ALGORITHMS; CLASSIFICATION;

D O I：

10.1109/ISDA.2009.79

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Feature selection is an essential task in data mining because it makes it possible not only to reduce computational times and storage requirements, but also to favor model improvement and better data understanding. In this work, we analyze three methods for unsupervised feature selection that are based on the clustering of features for redundancy removal. We report experimental results obtained in ten datasets that illustrate practical scenarios of particular interest, in which one method may be preferred over another. In order to provide some reassurance about the validity and non-randomness of the obtained results, we also present the results of statistical tests.

引用

页码：993 / 1000

页数：8

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