Quantum Chaotic Honey Badger Algorithm for Feature Selection
Determining the most relevant features is a critical pre-processing step in various fields to enhance prediction. To address this issue, a set of feature selection (FS) techniques have been proposed; however, they still have certain limitations. For example, they may focus on nearby points, which lo...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/11/21/3463 |
| _version_ | 1797468494396129280 |
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| author | Samah Alshathri Mohamed Abd Elaziz Dalia Yousri Osama Farouk Hassan Rehab Ali Ibrahim |
| author_facet | Samah Alshathri Mohamed Abd Elaziz Dalia Yousri Osama Farouk Hassan Rehab Ali Ibrahim |
| author_sort | Samah Alshathri |
| collection | DOAJ |
| description | Determining the most relevant features is a critical pre-processing step in various fields to enhance prediction. To address this issue, a set of feature selection (FS) techniques have been proposed; however, they still have certain limitations. For example, they may focus on nearby points, which lowers classification accuracy because the chosen features may include noisy features. To take advantage of the benefits of the quantum-based optimization technique and the 2D chaotic Hénon map, we provide a modified version of the honey badger algorithm (HBA) called QCHBA. The ability of such strategies to strike a balance between exploitation and exploration while identifying the workable subset of pertinent features is the basis for employing them to enhance HBA. The effectiveness of QCHBA was evaluated in a series of experiments conducted using eighteen datasets involving comparison with recognized FS techniques. The results indicate high efficiency of the QCHBA among the datasets using various performance criteria. |
| first_indexed | 2024-03-09T19:08:16Z |
| format | Article |
| id | doaj.art-4d36d9d168074f0095abee4746643f65 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-09T19:08:16Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-4d36d9d168074f0095abee4746643f652023-11-24T04:24:22ZengMDPI AGElectronics2079-92922022-10-011121346310.3390/electronics11213463Quantum Chaotic Honey Badger Algorithm for Feature SelectionSamah Alshathri0Mohamed Abd Elaziz1Dalia Yousri 2Osama Farouk Hassan 3Rehab Ali Ibrahim 4Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaFaculty of Computer Science and Engineering, Galala University, Suez 435611, EgyptElectrical Engineering Department, Faculty of Engineering, Fayoum University, Faiyum 63514, EgyptDepartment of Information System, Faculty of Computers and Informatics, Suez Canal University, Ismailia 41522, EgyptDepartment of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, EgyptDetermining the most relevant features is a critical pre-processing step in various fields to enhance prediction. To address this issue, a set of feature selection (FS) techniques have been proposed; however, they still have certain limitations. For example, they may focus on nearby points, which lowers classification accuracy because the chosen features may include noisy features. To take advantage of the benefits of the quantum-based optimization technique and the 2D chaotic Hénon map, we provide a modified version of the honey badger algorithm (HBA) called QCHBA. The ability of such strategies to strike a balance between exploitation and exploration while identifying the workable subset of pertinent features is the basis for employing them to enhance HBA. The effectiveness of QCHBA was evaluated in a series of experiments conducted using eighteen datasets involving comparison with recognized FS techniques. The results indicate high efficiency of the QCHBA among the datasets using various performance criteria.https://www.mdpi.com/2079-9292/11/21/3463feature selectionhoney badger algorithm (HBA)quantum-based optimization technique2D chaotic Hénon map |
| spellingShingle | Samah Alshathri Mohamed Abd Elaziz Dalia Yousri Osama Farouk Hassan Rehab Ali Ibrahim Quantum Chaotic Honey Badger Algorithm for Feature Selection Electronics feature selection honey badger algorithm (HBA) quantum-based optimization technique 2D chaotic Hénon map |
| title | Quantum Chaotic Honey Badger Algorithm for Feature Selection |
| title_full | Quantum Chaotic Honey Badger Algorithm for Feature Selection |
| title_fullStr | Quantum Chaotic Honey Badger Algorithm for Feature Selection |
| title_full_unstemmed | Quantum Chaotic Honey Badger Algorithm for Feature Selection |
| title_short | Quantum Chaotic Honey Badger Algorithm for Feature Selection |
| title_sort | quantum chaotic honey badger algorithm for feature selection |
| topic | feature selection honey badger algorithm (HBA) quantum-based optimization technique 2D chaotic Hénon map |
| url | https://www.mdpi.com/2079-9292/11/21/3463 |
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