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Nearest-neighbour ensembles in lasso feature subspaces

Abstract : 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 study, the authors propose a robust classification algorithm that makes use of an ensemble of classifiers in lasso feature subspaces. The algorithm consists of two stages: the first is a lasso-based multiple feature subsets selection cycle, which tries to find a number of relevant and diverse feature subspaces; the second is an ensemble-based decision system that intends to preserve the classification performance in case of abrupt changes in the representation space. Experimental results on the two-class textured image segmentation problem prove the effectiveness of the proposed classification method.
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https://hal-utt.archives-ouvertes.fr/hal-02353673
Contributor : Jean-Baptiste Vu Van <>
Submitted on : Thursday, November 7, 2019 - 1:54:17 PM
Last modification on : Friday, November 8, 2019 - 1:39:49 AM

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Xiyan He, Pierre Beauseroy, André Smolarz. Nearest-neighbour ensembles in lasso feature subspaces. IET Computer Vision, IET, 2010, 4 (4), pp.306. ⟨10.1049/iet-cvi.2009.0056⟩. ⟨hal-02353673⟩

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