Efficient Feature Selection via Analysis of Relevance and Redundancy

Authors:
Lei Yu;Huan Liu
Affiliations:
-;-
Venue:
The Journal of Machine Learning Research
Year:
2004

Citing 27
Cited 177

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Abstract

Feature selection is applied to reduce the number of features in many applications where data has hundreds or thousands of features. Existing feature selection methods mainly focus on finding relevant features. In this paper, we show that feature relevance alone is insufficient for efficient feature selection of high-dimensional data. We define feature redundancy and propose to perform explicit redundancy analysis in feature selection. A new framework is introduced that decouples relevance analysis and redundancy analysis. We develop a correlation-based method for relevance and redundancy analysis, and conduct an empirical study of its efficiency and effectiveness comparing with representative methods.