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Granular cabin: An efficient solution to neighborhood learning in big data
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NSTL
Elsevier
Neighborhood Learning (NL) is a paradigm covering theories and techniques of neighborhood, which facilitates data organization, representation and generalization. While delivering impressive performances across various fields such as granular computing, cluster analysis, NL is argued to be computationally demanding, thereby limiting its utility and applicability. In this study, a simple and generic scheme named granular cabin is proposed for drastically speeding up the algorithmic implementation of NL. Specifically, this scheme is deployed to Neighborhood Rough Set (NRS) which is a typical NL methodology. And three major applications of NRS are concerned including approximation computation, neighborhood classification and feature selection. Extensive experiments demonstrate that NRS methodology enhanced by granular cabin consumes much less time. This study offers a promising solution that ensures the great potential of NL in big data. (c) 2021 Elsevier Inc. All rights reserved.
NSTL
Elsevier
