Binary Multi-Objective Grey Wolf Optimizer for Feature Selection in Classification

Feature selection or dimensionality reduction can be considered as a multi-objective minimization problem with two objectives: minimizing the number of features and minimizing the error rate simultaneously. Despite being a multi-objective problem, most existing approaches treat feature selection as...

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Main Authors: Al-Tashi, Q., Abdulkadir, S.J., Rais, H.M., Mirjalili, S., Alhussian, H., Ragab, M.G., Alqushaibi, A.
Format: Article
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.23324 /
Published: Institute of Electrical and Electronics Engineers Inc. 2020
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086737786&doi=10.1109%2fACCESS.2020.3000040&partnerID=40&md5=afb3ba464561276cf31fc5b1003d1f47
http://eprints.utp.edu.my/23324/
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spelling utp-eprints.233242021-08-19T07:25:20Z Binary Multi-Objective Grey Wolf Optimizer for Feature Selection in Classification Al-Tashi, Q. Abdulkadir, S.J. Rais, H.M. Mirjalili, S. Alhussian, H. Ragab, M.G. Alqushaibi, A. Feature selection or dimensionality reduction can be considered as a multi-objective minimization problem with two objectives: minimizing the number of features and minimizing the error rate simultaneously. Despite being a multi-objective problem, most existing approaches treat feature selection as a single-objective optimization problem. Recently, Multi-objective Grey Wolf optimizer (MOGWO) was proposed to solve multi-objective optimization problem. However, MOGWO was originally designed for continuous optimization problems and hence, it cannot be utilized directly to solve multi-objective feature selection problems which are inherently discrete in nature. Therefore, in this research, a binary version of MOGWO based on sigmoid transfer function called BMOGW-S is developed to optimize feature selection problems. A wrapper based Artificial Neural Network (ANN) is used to assess the classification performance of a subset of selected features. To validate the performance of the proposed method, 15 standard benchmark datasets from the UCI repository are employed. The proposed BMOGWO-S was compared with MOGWO with a tanh transfer function and Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-objective Particle Swarm Optimization (MOPSO). The results showed that the proposed BMOGWO-S can effectively determine a set of non-dominated solutions. The proposed method outperforms the existing multi-objective approaches in most cases in terms of features reduction as well as classification error rate while benefiting from a lower computational cost. © 2013 IEEE. Institute of Electrical and Electronics Engineers Inc. 2020 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086737786&doi=10.1109%2fACCESS.2020.3000040&partnerID=40&md5=afb3ba464561276cf31fc5b1003d1f47 Al-Tashi, Q. and Abdulkadir, S.J. and Rais, H.M. and Mirjalili, S. and Alhussian, H. and Ragab, M.G. and Alqushaibi, A. (2020) Binary Multi-Objective Grey Wolf Optimizer for Feature Selection in Classification. IEEE Access, 8 . pp. 106247-106263. http://eprints.utp.edu.my/23324/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description Feature selection or dimensionality reduction can be considered as a multi-objective minimization problem with two objectives: minimizing the number of features and minimizing the error rate simultaneously. Despite being a multi-objective problem, most existing approaches treat feature selection as a single-objective optimization problem. Recently, Multi-objective Grey Wolf optimizer (MOGWO) was proposed to solve multi-objective optimization problem. However, MOGWO was originally designed for continuous optimization problems and hence, it cannot be utilized directly to solve multi-objective feature selection problems which are inherently discrete in nature. Therefore, in this research, a binary version of MOGWO based on sigmoid transfer function called BMOGW-S is developed to optimize feature selection problems. A wrapper based Artificial Neural Network (ANN) is used to assess the classification performance of a subset of selected features. To validate the performance of the proposed method, 15 standard benchmark datasets from the UCI repository are employed. The proposed BMOGWO-S was compared with MOGWO with a tanh transfer function and Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-objective Particle Swarm Optimization (MOPSO). The results showed that the proposed BMOGWO-S can effectively determine a set of non-dominated solutions. The proposed method outperforms the existing multi-objective approaches in most cases in terms of features reduction as well as classification error rate while benefiting from a lower computational cost. © 2013 IEEE.
format Article
author Al-Tashi, Q.
Abdulkadir, S.J.
Rais, H.M.
Mirjalili, S.
Alhussian, H.
Ragab, M.G.
Alqushaibi, A.
spellingShingle Al-Tashi, Q.
Abdulkadir, S.J.
Rais, H.M.
Mirjalili, S.
Alhussian, H.
Ragab, M.G.
Alqushaibi, A.
Binary Multi-Objective Grey Wolf Optimizer for Feature Selection in Classification
author_sort Al-Tashi, Q.
title Binary Multi-Objective Grey Wolf Optimizer for Feature Selection in Classification
title_short Binary Multi-Objective Grey Wolf Optimizer for Feature Selection in Classification
title_full Binary Multi-Objective Grey Wolf Optimizer for Feature Selection in Classification
title_fullStr Binary Multi-Objective Grey Wolf Optimizer for Feature Selection in Classification
title_full_unstemmed Binary Multi-Objective Grey Wolf Optimizer for Feature Selection in Classification
title_sort binary multi-objective grey wolf optimizer for feature selection in classification
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2020
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086737786&doi=10.1109%2fACCESS.2020.3000040&partnerID=40&md5=afb3ba464561276cf31fc5b1003d1f47
http://eprints.utp.edu.my/23324/
_version_ 1741196657477812224
score 11.62408

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