Title: ReliefMSS: a variation on a feature ranking ReliefF algorithm

Authors: Salim Chikhi, Sadek Benhammada

Addresses: Computer Science Department, Mentouri University, MISC Laboratory, route de Aïn El Bey, Constantine 25017, Algeria. ' Computer Science Department, Mentouri University, MISC Laboratory, route de Aïn El Bey, Constantine 25017, Algeria

Abstract: Relief algorithms are successful attribute estimators. They are able to detect conditional dependencies between attributes and provide a unified view on the attribute estimation. In this paper, we propose a variant of ReliefF algorithm: ReliefMSS. We analyse the ReliefMSS parameters and compare ReliefF and ReliefMSS performances as regards the number of iterations, the number of random attributes, the noise effect, the number of nearest neighbours and the number of examples presented. We find that for the most of these parameters, ReliefMSS is better than ReliefF.

Keywords: feature selection; relief algorithms; number of nearest neighbours; attribute estimators.

DOI: 10.1504/IJBIDM.2009.029085

International Journal of Business Intelligence and Data Mining, 2009 Vol.4 No.3/4, pp.375 - 390

Published online: 03 Nov 2009 *

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