Please use this identifier to cite or link to this item: https://idr.l2.nitk.ac.in/jspui/handle/123456789/7132
Title: A single program multiple data algorithm for feature selection
Authors: Chanduka, B.
Gangavarapu, T.
Jaidhar, C.D.
Issue Date: 2020
Citation: Advances in Intelligent Systems and Computing, 2020, Vol.940, , pp.662-672
Abstract: Feature 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.
URI: http://idr.nitk.ac.in/jspui/handle/123456789/7132
Appears in Collections:2. Conference Papers

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