Staff View: Improved Manta Ray Foraging Optimizer-based SVM for Feature Selection Problems: A Medical Case Study

Improved Manta Ray Foraging Optimizer-based SVM for Feature Selection Problems: A Medical Case Study

Support Vector Machine (SVM) has become one of the traditional machine learning algorithms the most used in prediction and classification tasks. However, its behavior strongly depends on some parameters, making tuning these parameters a sensitive step to maintain a good performance. On the other han...

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Main Authors:	Got, Adel, Zouache, Djaafar, Moussaoui, Abdelouahab, Laith, Abualigah *, Alsayat, Ahmed
Format:	Article
Published:	Springer 2024
Subjects:	Q Science (General) QL Zoology TA Engineering (General). Civil engineering (General)
Online Access:	http://eprints.sunway.edu.my/2785/ https://link.springer.com/article/10.1007/s42235-023-00436-9
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id	my.sunway.eprints.2785
record_format	eprints
spelling	my.sunway.eprints.27852024-07-12T00:12:05Z http://eprints.sunway.edu.my/2785/ Improved Manta Ray Foraging Optimizer-based SVM for Feature Selection Problems: A Medical Case Study Got, Adel Zouache, Djaafar Moussaoui, Abdelouahab Laith, Abualigah * Alsayat, Ahmed Q Science (General) QL Zoology TA Engineering (General). Civil engineering (General) Support Vector Machine (SVM) has become one of the traditional machine learning algorithms the most used in prediction and classification tasks. However, its behavior strongly depends on some parameters, making tuning these parameters a sensitive step to maintain a good performance. On the other hand, and as any other classifier, the performance of SVM is also affected by the input set of features used to build the learning model, which makes the selection of relevant features an important task not only to preserve a good classification accuracy but also to reduce the dimensionality of datasets. In this paper, the MRFO + SVM algorithm is introduced by investigating the recent manta ray foraging optimizer to fine-tune the SVM parameters and identify the optimal feature subset simultaneously. The proposed approach is validated and compared with four SVM-based algorithms over eight benchmarking datasets. Additionally, it is applied to a disease Covid-19 dataset. The experimental results show the high ability of the proposed algorithm to find the appropriate SVM’s parameters, and its acceptable performance to deal with feature selection problem. Springer 2024 Article PeerReviewed Got, Adel and Zouache, Djaafar and Moussaoui, Abdelouahab and Laith, Abualigah * and Alsayat, Ahmed (2024) Improved Manta Ray Foraging Optimizer-based SVM for Feature Selection Problems: A Medical Case Study. Journal of Bionic Engineering, 21. pp. 409-425. ISSN 2543-2141 https://link.springer.com/article/10.1007/s42235-023-00436-9 10.1007/s42235-023-00436-9
institution	Sunway University
building	Sunway Campus Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Sunway University
content_source	Sunway Institutional Repository
url_provider	http://eprints.sunway.edu.my/
topic	Q Science (General) QL Zoology TA Engineering (General). Civil engineering (General)
spellingShingle	Q Science (General) QL Zoology TA Engineering (General). Civil engineering (General) Got, Adel Zouache, Djaafar Moussaoui, Abdelouahab Laith, Abualigah * Alsayat, Ahmed Improved Manta Ray Foraging Optimizer-based SVM for Feature Selection Problems: A Medical Case Study
description	Support Vector Machine (SVM) has become one of the traditional machine learning algorithms the most used in prediction and classification tasks. However, its behavior strongly depends on some parameters, making tuning these parameters a sensitive step to maintain a good performance. On the other hand, and as any other classifier, the performance of SVM is also affected by the input set of features used to build the learning model, which makes the selection of relevant features an important task not only to preserve a good classification accuracy but also to reduce the dimensionality of datasets. In this paper, the MRFO + SVM algorithm is introduced by investigating the recent manta ray foraging optimizer to fine-tune the SVM parameters and identify the optimal feature subset simultaneously. The proposed approach is validated and compared with four SVM-based algorithms over eight benchmarking datasets. Additionally, it is applied to a disease Covid-19 dataset. The experimental results show the high ability of the proposed algorithm to find the appropriate SVM’s parameters, and its acceptable performance to deal with feature selection problem.
format	Article
author	Got, Adel Zouache, Djaafar Moussaoui, Abdelouahab Laith, Abualigah * Alsayat, Ahmed
author_facet	Got, Adel Zouache, Djaafar Moussaoui, Abdelouahab Laith, Abualigah * Alsayat, Ahmed
author_sort	Got, Adel
title	Improved Manta Ray Foraging Optimizer-based SVM for Feature Selection Problems: A Medical Case Study
title_short	Improved Manta Ray Foraging Optimizer-based SVM for Feature Selection Problems: A Medical Case Study
title_full	Improved Manta Ray Foraging Optimizer-based SVM for Feature Selection Problems: A Medical Case Study
title_fullStr	Improved Manta Ray Foraging Optimizer-based SVM for Feature Selection Problems: A Medical Case Study
title_full_unstemmed	Improved Manta Ray Foraging Optimizer-based SVM for Feature Selection Problems: A Medical Case Study
title_sort	improved manta ray foraging optimizer-based svm for feature selection problems: a medical case study
publisher	Springer
publishDate	2024
url	http://eprints.sunway.edu.my/2785/ https://link.springer.com/article/10.1007/s42235-023-00436-9
_version_	1805893811985973248
score	13.23243

Improved Manta Ray Foraging Optimizer-based SVM for Feature Selection Problems: A Medical Case Study

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