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      Elitist Binary Wolf Search Algorithm for Heuristic Feature Selection in High-Dimensional Bioinformatics Datasets

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          Abstract

          Due to the high-dimensional characteristics of dataset, we propose a new method based on the Wolf Search Algorithm (WSA) for optimising the feature selection problem. The proposed approach uses the natural strategy established by Charles Darwin; that is, ‘ It is not the strongest of the species that survives, but the most adaptable’. This means that in the evolution of a swarm, the elitists are motivated to quickly obtain more and better resources. The memory function helps the proposed method to avoid repeat searches for the worst position in order to enhance the effectiveness of the search, while the binary strategy simplifies the feature selection problem into a similar problem of function optimisation. Furthermore, the wrapper strategy gathers these strengthened wolves with the classifier of extreme learning machine to find a sub-dataset with a reasonable number of features that offers the maximum correctness of global classification models. The experimental results from the six public high-dimensional bioinformatics datasets tested demonstrate that the proposed method can best some of the conventional feature selection methods up to 29% in classification accuracy, and outperform previous WSAs by up to 99.81% in computational time.

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          Extreme learning machine: Theory and applications

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            Wrappers for feature subset selection

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                Author and article information

                Contributors
                Kelvin.Wong@westernsydney.edu.au
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                28 June 2017
                28 June 2017
                2017
                : 7
                Affiliations
                [1 ]Department of Computer and Information Science, University of Macau, Macau SAR, China
                [2 ]ISNI 0000 0004 4902 0432, GRID grid.1005.4, School of Computer Science and Engineering, , University of New South Wales, ; New South Wales, Australia
                [3 ]ISNI 0000 0000 9360 9165, GRID grid.412114.3, Department of Information Technology, , Durban University of Technology, ; Durban, South Africa
                [4 ]ISNI 0000 0004 1936 834X, GRID grid.1013.3, School of Medicine, , Western Sydney University, ; New South Wales, Australia
                [5 ]ISNI 0000 0004 1936 7304, GRID grid.1010.0, Centre for Biomedical Engineering, , School of Electrical & Electronic Engineering, University of Adelaide, ; Adelaide, Australia
                Article
                4037
                10.1038/s41598-017-04037-5
                5489518
                28659577
                © The Author(s) 2017

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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