ReliefMSS: a variation on a feature ranking ReliefF algorithm
by Salim Chikhi, Sadek Benhammada
International Journal of Business Intelligence and Data Mining (IJBIDM), Vol. 4, No. 3/4, 2009

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.

Online publication date: Tue, 03-Nov-2009

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