NDLI: A Comparative Study on the Use of Correlation Coefficients for Redundant Feature Elimination

Content Provider	IEEE Xplore Digital Library
Author	Jaskowiak, P.A. Campello, R.J.G.B. Covões, T.F. Hruschka, E.R.
Copyright Year	2010
Abstract	Simplified Silhouette Filter (SSF) is a recently introduced feature selection method that automatically estimates the number of features to be selected. To do so, a sampling strategy is combined with a clustering algorithm that seeks clusters of correlated (potentially redundant) features. It is well known that the choice of a similarity measure may have great impact in clustering results. As a consequence, in this application scenario, this choice may have great impact in the feature subset to be selected. In this paper we study six correlation coefficients as similarity measures in the clustering stage of SSF, thus giving rise to several variants of the original method. The obtained results show that, in particular scenarios, some correlation measures select fewer features than others, while providing accurate classifiers.
Starting Page	13
Ending Page	18
File Size	227539
Page Count	6
File Format	PDF
ISBN	9781424483914
ISSN	15224899
DOI	10.1109/SBRN.2010.11
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-10-23
Publisher Place	Brazil
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Correlation Accuracy Clustering algorithms Niobium Partitioning algorithms Training Equations Classification Feature Selection Correlation Coefficients
Content Type	Text
Resource Type	Article

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