Cover Image

Inversed Control Parameter in Whale Optimization Algorithm and Grey Wolf Optimizer for Wrapper-Based Feature Selection: A Comparative Study

Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO) are well-perform metaheuristic algorithms used by various researchers in solving feature selection problems. Yet, the slow convergence speed issue in the Whale Optimization Algorithm and Grey Wolf Optimizer could demote the performance...

Full description

Saved in:
Bibliographic Details
Main Authors: Li Yu Yab, Li Yu Yab, Wahid, Noorhaniza, A Hamid, Rahayu
Format: Article
Language:English
Published: JOIV 2023
Subjects:
T Technology (General)
Online Access:http://eprints.uthm.edu.my/10528/1/J16216_dbf3f4d7159f113fecb17ccb83436cd7.pdf
http://eprints.uthm.edu.my/10528/
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.uthm.eprints.10528
record_format eprints
spelling my.uthm.eprints.105282024-01-03T01:35:09Z http://eprints.uthm.edu.my/10528/ Inversed Control Parameter in Whale Optimization Algorithm and Grey Wolf Optimizer for Wrapper-Based Feature Selection: A Comparative Study Li Yu Yab, Li Yu Yab Wahid, Noorhaniza A Hamid, Rahayu T Technology (General) Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO) are well-perform metaheuristic algorithms used by various researchers in solving feature selection problems. Yet, the slow convergence speed issue in the Whale Optimization Algorithm and Grey Wolf Optimizer could demote the performance of feature selection and classification accuracy. Therefore, to overcome this issue, a modified WOA (mWOA) and modified GWO (mGWO) for wrapper-based feature selection were proposed in this study. The proposed mWOA and mGWO were given a new inversed control parameter expected to enable more search areas for the search agents in the early phase of the algorithms, resulting in a faster convergence speed. This comparative study aims to investigate and compare the effectiveness of the inversed control parameter in the proposed methods against the original algorithms in terms of the number of selected features and the classification accuracy. The proposed methods were implemented in MATLAB where 12 datasets with different dimensionality from the UCI repository were used. kNN was chosen as the classifier to evaluate the classification accuracy of the selected features. Based on the experimental results, mGWO did not show significant improvements in feature reduction and maintained similar accuracy as the original GWO. On the contrary, mWOA outperformed the original WOA regarding the two criteria mentioned, even on high-dimensional datasets. Evaluating the execution time of the proposed methods, utilizing different classifiers, and hybridizing proposed methods with other metaheuristic algorithms to solve feature selection problems would be future works worth exploring. JOIV 2023 Article PeerReviewed text en http://eprints.uthm.edu.my/10528/1/J16216_dbf3f4d7159f113fecb17ccb83436cd7.pdf Li Yu Yab, Li Yu Yab and Wahid, Noorhaniza and A Hamid, Rahayu (2023) Inversed Control Parameter in Whale Optimization Algorithm and Grey Wolf Optimizer for Wrapper-Based Feature Selection: A Comparative Study. INTERNATIONAL JOURNAL ON INFORMATICS VISUALIZATION, 7 (2). pp. 477-486.
institution Universiti Tun Hussein Onn Malaysia
building UTHM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tun Hussein Onn Malaysia
content_source UTHM Institutional Repository
url_provider http://eprints.uthm.edu.my/
language English
topic T Technology (General)
spellingShingle T Technology (General)
Li Yu Yab, Li Yu Yab
Wahid, Noorhaniza
A Hamid, Rahayu
Inversed Control Parameter in Whale Optimization Algorithm and Grey Wolf Optimizer for Wrapper-Based Feature Selection: A Comparative Study
description Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO) are well-perform metaheuristic algorithms used by various researchers in solving feature selection problems. Yet, the slow convergence speed issue in the Whale Optimization Algorithm and Grey Wolf Optimizer could demote the performance of feature selection and classification accuracy. Therefore, to overcome this issue, a modified WOA (mWOA) and modified GWO (mGWO) for wrapper-based feature selection were proposed in this study. The proposed mWOA and mGWO were given a new inversed control parameter expected to enable more search areas for the search agents in the early phase of the algorithms, resulting in a faster convergence speed. This comparative study aims to investigate and compare the effectiveness of the inversed control parameter in the proposed methods against the original algorithms in terms of the number of selected features and the classification accuracy. The proposed methods were implemented in MATLAB where 12 datasets with different dimensionality from the UCI repository were used. kNN was chosen as the classifier to evaluate the classification accuracy of the selected features. Based on the experimental results, mGWO did not show significant improvements in feature reduction and maintained similar accuracy as the original GWO. On the contrary, mWOA outperformed the original WOA regarding the two criteria mentioned, even on high-dimensional datasets. Evaluating the execution time of the proposed methods, utilizing different classifiers, and hybridizing proposed methods with other metaheuristic algorithms to solve feature selection problems would be future works worth exploring.
format Article
author Li Yu Yab, Li Yu Yab
Wahid, Noorhaniza
A Hamid, Rahayu
author_facet Li Yu Yab, Li Yu Yab
Wahid, Noorhaniza
A Hamid, Rahayu
author_sort Li Yu Yab, Li Yu Yab
title Inversed Control Parameter in Whale Optimization Algorithm and Grey Wolf Optimizer for Wrapper-Based Feature Selection: A Comparative Study
title_short Inversed Control Parameter in Whale Optimization Algorithm and Grey Wolf Optimizer for Wrapper-Based Feature Selection: A Comparative Study
title_full Inversed Control Parameter in Whale Optimization Algorithm and Grey Wolf Optimizer for Wrapper-Based Feature Selection: A Comparative Study
title_fullStr Inversed Control Parameter in Whale Optimization Algorithm and Grey Wolf Optimizer for Wrapper-Based Feature Selection: A Comparative Study
title_full_unstemmed Inversed Control Parameter in Whale Optimization Algorithm and Grey Wolf Optimizer for Wrapper-Based Feature Selection: A Comparative Study
title_sort inversed control parameter in whale optimization algorithm and grey wolf optimizer for wrapper-based feature selection: a comparative study
publisher JOIV
publishDate 2023
url http://eprints.uthm.edu.my/10528/1/J16216_dbf3f4d7159f113fecb17ccb83436cd7.pdf
http://eprints.uthm.edu.my/10528/
_version_ 1787137849161678848
score 13.211869

Similar Items