An Electric Fish-Based Arithmetic Optimization Algorithm for Feature Selection
With the widespread use of intelligent information systems, a massive amount of data with lots of irrelevant, noisy, and redundant features are collected; moreover, many features should be handled. Therefore, introducing an efficient feature selection (FS) approach becomes a challenging aim. In the...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-09-01
|
| Series: | Entropy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1099-4300/23/9/1189 |
| _version_ | 1797519444549828608 |
|---|---|
| author | Rehab Ali Ibrahim Laith Abualigah Ahmed A. Ewees Mohammed A. A. Al-qaness Dalia Yousri Samah Alshathri Mohamed Abd Elaziz |
| author_facet | Rehab Ali Ibrahim Laith Abualigah Ahmed A. Ewees Mohammed A. A. Al-qaness Dalia Yousri Samah Alshathri Mohamed Abd Elaziz |
| author_sort | Rehab Ali Ibrahim |
| collection | DOAJ |
| description | With the widespread use of intelligent information systems, a massive amount of data with lots of irrelevant, noisy, and redundant features are collected; moreover, many features should be handled. Therefore, introducing an efficient feature selection (FS) approach becomes a challenging aim. In the recent decade, various artificial methods and swarm models inspired by biological and social systems have been proposed to solve different problems, including FS. Thus, in this paper, an innovative approach is proposed based on a hybrid integration between two intelligent algorithms, Electric fish optimization (EFO) and the arithmetic optimization algorithm (AOA), to boost the exploration stage of EFO to process the high dimensional FS problems with a remarkable convergence speed. The proposed EFOAOA is examined with eighteen datasets for different real-life applications. The EFOAOA results are compared with a set of recent state-of-the-art optimizers using a set of statistical metrics and the Friedman test. The comparisons show the positive impact of integrating the AOA operator in the EFO, as the proposed EFOAOA can identify the most important features with high accuracy and efficiency. Compared to the other FS methods whereas, it got the lowest features number and the highest accuracy in 50% and 67% of the datasets, respectively. |
| first_indexed | 2024-03-10T07:41:52Z |
| format | Article |
| id | doaj.art-bcc4d8ac00264276908a955695f2540e |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T07:41:52Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-bcc4d8ac00264276908a955695f2540e2023-11-22T12:58:06ZengMDPI AGEntropy1099-43002021-09-01239118910.3390/e23091189An Electric Fish-Based Arithmetic Optimization Algorithm for Feature SelectionRehab Ali Ibrahim0Laith Abualigah1Ahmed A. Ewees2Mohammed A. A. Al-qaness3Dalia Yousri4Samah Alshathri5Mohamed Abd Elaziz6Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, EgyptFaculty of Computer Sciences and Informatics, Amman Arab University, Amman 11953, JordanDepartment of Computer, Damietta University, Damietta 34517, EgyptState Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaElectrical Engineering Department, Faculty of Engineering, Fayoum University, Fayoum 63514, EgyptDepartment of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 84428, Saudi ArabiaDepartment of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, EgyptWith the widespread use of intelligent information systems, a massive amount of data with lots of irrelevant, noisy, and redundant features are collected; moreover, many features should be handled. Therefore, introducing an efficient feature selection (FS) approach becomes a challenging aim. In the recent decade, various artificial methods and swarm models inspired by biological and social systems have been proposed to solve different problems, including FS. Thus, in this paper, an innovative approach is proposed based on a hybrid integration between two intelligent algorithms, Electric fish optimization (EFO) and the arithmetic optimization algorithm (AOA), to boost the exploration stage of EFO to process the high dimensional FS problems with a remarkable convergence speed. The proposed EFOAOA is examined with eighteen datasets for different real-life applications. The EFOAOA results are compared with a set of recent state-of-the-art optimizers using a set of statistical metrics and the Friedman test. The comparisons show the positive impact of integrating the AOA operator in the EFO, as the proposed EFOAOA can identify the most important features with high accuracy and efficiency. Compared to the other FS methods whereas, it got the lowest features number and the highest accuracy in 50% and 67% of the datasets, respectively.https://www.mdpi.com/1099-4300/23/9/1189swarm modelsfeature selection (FS)metaheuristic (MH)electric fish optimization (EFO)arithmetic optimization algorithm (AOA) |
| spellingShingle | Rehab Ali Ibrahim Laith Abualigah Ahmed A. Ewees Mohammed A. A. Al-qaness Dalia Yousri Samah Alshathri Mohamed Abd Elaziz An Electric Fish-Based Arithmetic Optimization Algorithm for Feature Selection Entropy swarm models feature selection (FS) metaheuristic (MH) electric fish optimization (EFO) arithmetic optimization algorithm (AOA) |
| title | An Electric Fish-Based Arithmetic Optimization Algorithm for Feature Selection |
| title_full | An Electric Fish-Based Arithmetic Optimization Algorithm for Feature Selection |
| title_fullStr | An Electric Fish-Based Arithmetic Optimization Algorithm for Feature Selection |
| title_full_unstemmed | An Electric Fish-Based Arithmetic Optimization Algorithm for Feature Selection |
| title_short | An Electric Fish-Based Arithmetic Optimization Algorithm for Feature Selection |
| title_sort | electric fish based arithmetic optimization algorithm for feature selection |
| topic | swarm models feature selection (FS) metaheuristic (MH) electric fish optimization (EFO) arithmetic optimization algorithm (AOA) |
| url | https://www.mdpi.com/1099-4300/23/9/1189 |
| work_keys_str_mv | AT rehabaliibrahim anelectricfishbasedarithmeticoptimizationalgorithmforfeatureselection AT laithabualigah anelectricfishbasedarithmeticoptimizationalgorithmforfeatureselection AT ahmedaewees anelectricfishbasedarithmeticoptimizationalgorithmforfeatureselection AT mohammedaaalqaness anelectricfishbasedarithmeticoptimizationalgorithmforfeatureselection AT daliayousri anelectricfishbasedarithmeticoptimizationalgorithmforfeatureselection AT samahalshathri anelectricfishbasedarithmeticoptimizationalgorithmforfeatureselection AT mohamedabdelaziz anelectricfishbasedarithmeticoptimizationalgorithmforfeatureselection AT rehabaliibrahim electricfishbasedarithmeticoptimizationalgorithmforfeatureselection AT laithabualigah electricfishbasedarithmeticoptimizationalgorithmforfeatureselection AT ahmedaewees electricfishbasedarithmeticoptimizationalgorithmforfeatureselection AT mohammedaaalqaness electricfishbasedarithmeticoptimizationalgorithmforfeatureselection AT daliayousri electricfishbasedarithmeticoptimizationalgorithmforfeatureselection AT samahalshathri electricfishbasedarithmeticoptimizationalgorithmforfeatureselection AT mohamedabdelaziz electricfishbasedarithmeticoptimizationalgorithmforfeatureselection |