A comparative study on the use of correlation coefficients for redundant feature elimination

Pablo A. Jaskowiak, Ricardo J.G.B. Campello, Thiago F. Covões, Eduardo R. Hruschka

Publikation: Kapitel i bog/rapport/konference-proceedingKonferencebidrag i proceedingsForskningpeer review

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.

OriginalsprogEngelsk
TitelProceedings - 2010 11th Brazilian Symposium on Neural Networks, SBRN 2010
ForlagIEEE
Publikationsdato2010
Sider13-18
Artikelnummer5715206
ISBN (Trykt)9780769542102
DOI
StatusUdgivet - 2010
Udgivet eksterntJa
Begivenhed2010 11th Brazilian Symposium on Neural Networks, SBRN 2010 - Sao Paulo, Brasilien
Varighed: 23. okt. 201028. okt. 2010

Konference

Konference2010 11th Brazilian Symposium on Neural Networks, SBRN 2010
Land/OmrådeBrasilien
BySao Paulo
Periode23/10/201028/10/2010
SponsorBrazilian Computer Society (SBC)
NavnProceedings - Brazilian Symposium on Neural Networks, SBRN
ISSN1522-4899

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