Nearest-neighbour ensembles in lasso feature subspaces

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Nearest-neighbour ensembles in lasso feature subspaces

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© The Institution of Engineering and Technology
Published

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.

Inspec keywords: image texture; image segmentation

Other keywords: lasso feature subspaces; nearest-neighbour ensembles; textured image segmentation problem; representation space; least absolute shrinkage and selection operator

Subjects: Computer vision and image processing techniques; Optical, image and video signal processing

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