A New Hybrid Filter-Wrapper Feature Selection using Equilibrium Optimizer and Simulated Annealing
Data dimensions and networks have grown exponentially with the Internet and communications. The challenge of high-dimensional data is increasing for machine learning and data science. This paper presents a hybrid filter-wrapper feature selection method based on Equilibrium Optimization (EO) and Simu...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Shahid Bahonar University of Kerman
2023-11-01
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| Series: | Journal of Mahani Mathematical Research |
| Subjects: | |
| Online Access: | https://jmmrc.uk.ac.ir/article_3852_3fc1f60f32f24f9eb6dabbc980a54662.pdf |
| _version_ | 1827347367768096768 |
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| author | Mohammad Ansari Shiri Mohammad Omidi Najme Mansouri |
| author_facet | Mohammad Ansari Shiri Mohammad Omidi Najme Mansouri |
| author_sort | Mohammad Ansari Shiri |
| collection | DOAJ |
| description | Data dimensions and networks have grown exponentially with the Internet and communications. The challenge of high-dimensional data is increasing for machine learning and data science. This paper presents a hybrid filter-wrapper feature selection method based on Equilibrium Optimization (EO) and Simulated Annealing (SA). The proposed algorithm is named Filter-Wrapper Binary Equilibrium Optimizer Simulated Annealing (FWBEOSA). We used SA to solve the local optimal problem so that EO could be more accurate and better able to select the best subset of features. FWBEOSA utilizes a filtering phase that increases accuracy as well as reduces the number of selected features. The proposed method is evaluated on 17 standard UCI datasets using Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) classifiers and compared with ten state-of-the-art algorithms (i.e., Binary Equilibrium Optimizer (BEO), Binary Gray Wolf Optimization (BGWO), Binary Swarm Slap Algorithm (BSSA), Binary Genetic Algorithm (BGA), Binary Particle Swarm Optimization (BPSO), Binary Social Mimic Optimization (BSMO), Binary Atom Search Optimization (BASO), Modified Flower Pollination Algorithm (MFPA), Bar Bones Particle Swarm Optimization (BBPSO) and Two-phase Mutation Gray Wolf Optimization (TMGWO)). Based on the results of the SVM classification, the highest level of accuracy was achieved in 13 out of 17 data sets (76%), and the lowest number of selected features was achieved in 15 out of 17 data sets (88%). Furthermore, the proposed algorithm using class KNN achieved the highest accuracy rate in 14 datasets (82%) and the lowest selective feature rate in 13 datasets (76%). |
| first_indexed | 2024-03-07T23:48:32Z |
| format | Article |
| id | doaj.art-cc2bf4981ce94a1aac5c4e24acc73be4 |
| institution | Directory Open Access Journal |
| issn | 2251-7952 2645-4505 |
| language | English |
| last_indexed | 2024-03-07T23:48:32Z |
| publishDate | 2023-11-01 |
| publisher | Shahid Bahonar University of Kerman |
| record_format | Article |
| series | Journal of Mahani Mathematical Research |
| spelling | doaj.art-cc2bf4981ce94a1aac5c4e24acc73be42024-02-19T09:29:20ZengShahid Bahonar University of KermanJournal of Mahani Mathematical Research2251-79522645-45052023-11-0113129333210.22103/jmmr.2023.21150.14113852A New Hybrid Filter-Wrapper Feature Selection using Equilibrium Optimizer and Simulated AnnealingMohammad Ansari Shiri0Mohammad Omidi1Najme Mansouri2Department of Computer Science, Shahid Bahonar University of Kerman, Kerman, IranDepartment of Computer Science, Shahid Bahonar University of Kerman, Kerman, IranDepartment of Computer Science, Shahid Bahonar University of Kerman, Kerman, IranData dimensions and networks have grown exponentially with the Internet and communications. The challenge of high-dimensional data is increasing for machine learning and data science. This paper presents a hybrid filter-wrapper feature selection method based on Equilibrium Optimization (EO) and Simulated Annealing (SA). The proposed algorithm is named Filter-Wrapper Binary Equilibrium Optimizer Simulated Annealing (FWBEOSA). We used SA to solve the local optimal problem so that EO could be more accurate and better able to select the best subset of features. FWBEOSA utilizes a filtering phase that increases accuracy as well as reduces the number of selected features. The proposed method is evaluated on 17 standard UCI datasets using Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) classifiers and compared with ten state-of-the-art algorithms (i.e., Binary Equilibrium Optimizer (BEO), Binary Gray Wolf Optimization (BGWO), Binary Swarm Slap Algorithm (BSSA), Binary Genetic Algorithm (BGA), Binary Particle Swarm Optimization (BPSO), Binary Social Mimic Optimization (BSMO), Binary Atom Search Optimization (BASO), Modified Flower Pollination Algorithm (MFPA), Bar Bones Particle Swarm Optimization (BBPSO) and Two-phase Mutation Gray Wolf Optimization (TMGWO)). Based on the results of the SVM classification, the highest level of accuracy was achieved in 13 out of 17 data sets (76%), and the lowest number of selected features was achieved in 15 out of 17 data sets (88%). Furthermore, the proposed algorithm using class KNN achieved the highest accuracy rate in 14 datasets (82%) and the lowest selective feature rate in 13 datasets (76%).https://jmmrc.uk.ac.ir/article_3852_3fc1f60f32f24f9eb6dabbc980a54662.pdffeature selectionequilibrium optimizersimulated annealingfilterwrapper |
| spellingShingle | Mohammad Ansari Shiri Mohammad Omidi Najme Mansouri A New Hybrid Filter-Wrapper Feature Selection using Equilibrium Optimizer and Simulated Annealing Journal of Mahani Mathematical Research feature selection equilibrium optimizer simulated annealing filter wrapper |
| title | A New Hybrid Filter-Wrapper Feature Selection using Equilibrium Optimizer and Simulated Annealing |
| title_full | A New Hybrid Filter-Wrapper Feature Selection using Equilibrium Optimizer and Simulated Annealing |
| title_fullStr | A New Hybrid Filter-Wrapper Feature Selection using Equilibrium Optimizer and Simulated Annealing |
| title_full_unstemmed | A New Hybrid Filter-Wrapper Feature Selection using Equilibrium Optimizer and Simulated Annealing |
| title_short | A New Hybrid Filter-Wrapper Feature Selection using Equilibrium Optimizer and Simulated Annealing |
| title_sort | new hybrid filter wrapper feature selection using equilibrium optimizer and simulated annealing |
| topic | feature selection equilibrium optimizer simulated annealing filter wrapper |
| url | https://jmmrc.uk.ac.ir/article_3852_3fc1f60f32f24f9eb6dabbc980a54662.pdf |
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