A New Quadratic Binary Harris Hawk Optimization for Feature Selection
Harris hawk optimization (HHO) is one of the recently proposed metaheuristic algorithms that has proven to be work more effectively in several challenging optimization tasks. However, the original HHO is developed to solve the continuous optimization problems, but not to the problems with binary var...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-10-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/8/10/1130 |
| _version_ | 1798004063228395520 |
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| author | Jingwei Too Abdul Rahim Abdullah Norhashimah Mohd Saad |
| author_facet | Jingwei Too Abdul Rahim Abdullah Norhashimah Mohd Saad |
| author_sort | Jingwei Too |
| collection | DOAJ |
| description | Harris hawk optimization (HHO) is one of the recently proposed metaheuristic algorithms that has proven to be work more effectively in several challenging optimization tasks. However, the original HHO is developed to solve the continuous optimization problems, but not to the problems with binary variables. This paper proposes the binary version of HHO (BHHO) to solve the feature selection problem in classification tasks. The proposed BHHO is equipped with an S-shaped or V-shaped transfer function to convert the continuous variable into a binary one. Moreover, another variant of HHO, namely quadratic binary Harris hawk optimization (QBHHO), is proposed to enhance the performance of BHHO. In this study, twenty-two datasets collected from the UCI machine learning repository are used to validate the performance of proposed algorithms. A comparative study is conducted to compare the effectiveness of QBHHO with other feature selection algorithms such as binary differential evolution (BDE), genetic algorithm (GA), binary multi-verse optimizer (BMVO), binary flower pollination algorithm (BFPA), and binary salp swarm algorithm (BSSA). The experimental results show the superiority of the proposed QBHHO in terms of classification performance, feature size, and fitness values compared to other algorithms. |
| first_indexed | 2024-04-11T12:17:34Z |
| format | Article |
| id | doaj.art-d68de125e0c34ee288664989360ba6fc |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-04-11T12:17:34Z |
| publishDate | 2019-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-d68de125e0c34ee288664989360ba6fc2022-12-22T04:24:12ZengMDPI AGElectronics2079-92922019-10-01810113010.3390/electronics8101130electronics8101130A New Quadratic Binary Harris Hawk Optimization for Feature SelectionJingwei Too0Abdul Rahim Abdullah1Norhashimah Mohd Saad2Fakulti Kejuruteraan Elektrik, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, MalaysiaFakulti Kejuruteraan Elektrik, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, MalaysiaFakulti Kejuruteraan Elektronik dan Kejuruteraan Komputer, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, MalaysiaHarris hawk optimization (HHO) is one of the recently proposed metaheuristic algorithms that has proven to be work more effectively in several challenging optimization tasks. However, the original HHO is developed to solve the continuous optimization problems, but not to the problems with binary variables. This paper proposes the binary version of HHO (BHHO) to solve the feature selection problem in classification tasks. The proposed BHHO is equipped with an S-shaped or V-shaped transfer function to convert the continuous variable into a binary one. Moreover, another variant of HHO, namely quadratic binary Harris hawk optimization (QBHHO), is proposed to enhance the performance of BHHO. In this study, twenty-two datasets collected from the UCI machine learning repository are used to validate the performance of proposed algorithms. A comparative study is conducted to compare the effectiveness of QBHHO with other feature selection algorithms such as binary differential evolution (BDE), genetic algorithm (GA), binary multi-verse optimizer (BMVO), binary flower pollination algorithm (BFPA), and binary salp swarm algorithm (BSSA). The experimental results show the superiority of the proposed QBHHO in terms of classification performance, feature size, and fitness values compared to other algorithms.https://www.mdpi.com/2079-9292/8/10/1130feature selectionbinary optimizationclassificationharris hawk optimizationquadratic transfer function |
| spellingShingle | Jingwei Too Abdul Rahim Abdullah Norhashimah Mohd Saad A New Quadratic Binary Harris Hawk Optimization for Feature Selection Electronics feature selection binary optimization classification harris hawk optimization quadratic transfer function |
| title | A New Quadratic Binary Harris Hawk Optimization for Feature Selection |
| title_full | A New Quadratic Binary Harris Hawk Optimization for Feature Selection |
| title_fullStr | A New Quadratic Binary Harris Hawk Optimization for Feature Selection |
| title_full_unstemmed | A New Quadratic Binary Harris Hawk Optimization for Feature Selection |
| title_short | A New Quadratic Binary Harris Hawk Optimization for Feature Selection |
| title_sort | new quadratic binary harris hawk optimization for feature selection |
| topic | feature selection binary optimization classification harris hawk optimization quadratic transfer function |
| url | https://www.mdpi.com/2079-9292/8/10/1130 |
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