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Nearest neighbor ensembles in lasso feature subspaces

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3 Author(s)
Xiyan He, ; Institut Charles Delaunay (FRE 2848 CNRS), Universite de Technologie de Troyes, 12, Rue Marie Curie, BP 2060, F-10010 France ; Beauseroy, Pierre ; Smolarz, Andre

The least absolute shrinkage and selection operator lasso is a promising feature selection technique. However, it has traditionally not been a focus of research in ensemble classification methods. In this paper, we propose an algorithm for building an ensemble of classifiers in lasso feature subspaces. The algorithm consists of two stages: the first is a lasso based feature subset selection cycle, which tries to find several discriminant feature subspaces; the second is an ensemble based decisional system that intends to preserve the classification performances in case of nonstationary perturbations. Experimental results on the two-class textured image segmentation problem assess the effectiveness of the proposed approach.

Published in:

Visual Information Engineering, 2008. VIE 2008. 5th International Conference on

Date of Conference:

July 29 2008-Aug. 1 2008