首页|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

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Support Vector Machine(SVM)has become one of the traditional machine learning algorithms the most used in predic-tion 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.

Support vector machineParameters tuningFeature selectionBioinspired algorithmsManta ray foraging optimizer

Adel Got、Djaafar Zouache、Abdelouahab Moussaoui、Laith Abualigah、Ahmed Alsayat

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Faculty of informatics,University of Science and Technology Houari Boumediene,Algiers,Algeria

LRIA Laboratory,University of Science and Technology Houari Boumediene,Algiers,Algeria

Computer Science Department,University of Mohamed El Bachir El Ibrahimi,Bordj Bou Arreridj,Algeria

Computer Science Department,University of Ferhat Abbas,Setif,Algeria

Computer Science Department,Prince Hussein Bin Abdullah Faculty for Information Technology,Al al-Bayt University,Mafraq 25113,Jordan

Department of Electrical and Computer Engineering,Lebanese American University,13-5053,Byblos,Lebanon

Hourani Center for Applied Scientific Research,Al-Ahliyya Amman University,Amman 19328,Jordan

MEU Research Unit,Middle East University,Amman 11831,Jordan

Applied science research center,Applied science private university,Amman 11931,Jordan

School of Computer Sciences,Universit

Department of Computer Science,College of Computer and Information Sciences,Jouf University,Jouf,Saudi Arabia

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2024

10.1007/s42235-023-00436-9

仿生工程学报(英文版)
吉林大学

仿生工程学报(英文版)

CSTPCDEI
影响因子：0.837
ISSN：1672-6529
年,卷(期)：2024.21(1)
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