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      Enhanced Feature Selection Based on Integration Containment Neighborhoods Rough Set Approximations and Binary Honey Badger Optimization

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          Abstract

          This article appoints a novel model of rough set approximations (RSA), namely, rough set approximation models build on containment neighborhoods RSA (CRSA), that generalize the traditional notions of RSA and obtain valuable consequences by minifying the boundary areas. To justify this extension, it is integrated with the binary version of the honey badger optimization (HBO) algorithm as a feature selection (FS) approach. The main target of using this extension is to assess the quality of selected features. To evaluate the performance of BHBO based on CRSA, a set of ten datasets is used. In addition, the results of BHOB are compared with other well-known FS approaches. The results show the superiority of CRSA over the traditional RS approximations. In addition, they illustrate the high ability of BHBO to improve the classification accuracy overall the compared methods in terms of performance metrics.

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          Most cited references57

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          Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems

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            Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm

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              Rough sets

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                Author and article information

                Contributors
                Journal
                Comput Intell Neurosci
                Comput Intell Neurosci
                cin
                Computational Intelligence and Neuroscience
                Hindawi
                1687-5265
                1687-5273
                2022
                10 March 2022
                : 2022
                : 3991870
                Affiliations
                1Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt
                2Academy of Scientific Research and Technology (ASRT), Ajman University, Cairo, Egypt
                3Faculty of Computer Science & Engineering, Galala University, Suze 435611, Egypt
                4Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman, UAE
                Author notes

                Academic Editor: Radu-Emil Precup

                Author information
                https://orcid.org/0000-0001-9861-0074
                https://orcid.org/0000-0002-7682-6269
                https://orcid.org/0000-0002-4340-1934
                Article
                10.1155/2022/3991870
                8930228
                35310578
                2f822896-dc51-4f95-be2a-24f72cc03f31
                Copyright © 2022 Rodyna A. Hosny et al.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 17 October 2021
                : 29 November 2021
                : 14 December 2021
                Funding
                Funded by: Academy of Scientific Research and Technology
                Award ID: 6684
                Categories
                Research Article

                Neurosciences
                Neurosciences

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