Please use this identifier to cite or link to this item: https://idr.l4.nitk.ac.in/jspui/handle/123456789/7132
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dc.contributor.authorChanduka, B.
dc.contributor.authorGangavarapu, T.
dc.contributor.authorJaidhar, C.D.
dc.date.accessioned2020-03-30T09:58:32Z-
dc.date.available2020-03-30T09:58:32Z-
dc.date.issued2020
dc.identifier.citationAdvances in Intelligent Systems and Computing, 2020, Vol.940, , pp.662-672en_US
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/7132-
dc.description.abstractFeature selection is a critical component in data science and has been the topic of research for many years. Advances in hardware and the availability of better multiprocessing platforms have enabled parallel computing to reach very high levels of performance. Minimum Redundancy Maximum Relevance (mRMR) is a powerful feature selection technique used in many applications. In this paper, we present a novel optimized Single Program Multiple Data (SPMD) approach to implement the mRMR algorithm with synchronous computation, optimum load balancing and greater speedup than task-parallel approaches. The experimental results presented using multiple synthesized datasets prove the efficiency and scalability of the proposed technique over original mRMR. � Springer Nature Switzerland AG 2020.en_US
dc.titleA single program multiple data algorithm for feature selectionen_US
dc.typeBook chapteren_US
Appears in Collections:2. Conference Papers

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