EBBA: An Enhanced Binary Bat Algorithm Integrated with Chaos Theory and Lévy Flight for Feature Selection
Feature selection can efficiently improve classification accuracy and reduce the dimension of datasets. However, feature selection is a challenging and complex task that requires a high-performance optimization algorithm. In this paper, we propose an enhanced binary bat algorithm (EBBA) which is ori...
| Principais autores: | , , |
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| Formato: | Artigo |
| Idioma: | English |
| Publicado em: |
MDPI AG
2022-06-01
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| coleção: | Future Internet |
| Assuntos: | |
| Acesso em linha: | https://www.mdpi.com/1999-5903/14/6/178 |
| _version_ | 1827660320115523584 |
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| author | Jinghui Feng Haopeng Kuang Lihua Zhang |
| author_facet | Jinghui Feng Haopeng Kuang Lihua Zhang |
| author_sort | Jinghui Feng |
| collection | DOAJ |
| description | Feature selection can efficiently improve classification accuracy and reduce the dimension of datasets. However, feature selection is a challenging and complex task that requires a high-performance optimization algorithm. In this paper, we propose an enhanced binary bat algorithm (EBBA) which is originated from the conventional binary bat algorithm (BBA) as the learning algorithm in a wrapper-based feature selection model. First, we model the feature selection problem and then transfer it as a fitness function. Then, we propose an EBBA for solving the feature selection problem. In EBBA, we introduce the Lévy flight-based global search method, population diversity boosting method and chaos-based loudness method to improve the BA and make it more applicable to feature selection problems. Finally, the simulations are conducted to evaluate the proposed EBBA and the simulation results demonstrate that the proposed EBBA outmatches other comparison benchmarks. Moreover, we also illustrate the effectiveness of the proposed improved factors by tests. |
| first_indexed | 2024-03-09T23:46:11Z |
| format | Article |
| id | doaj.art-d7dc7b73410c4de4a5390b111f3574bc |
| institution | Directory Open Access Journal |
| issn | 1999-5903 |
| language | English |
| last_indexed | 2024-03-09T23:46:11Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj.art-d7dc7b73410c4de4a5390b111f3574bc2023-11-23T16:43:38ZengMDPI AGFuture Internet1999-59032022-06-0114617810.3390/fi14060178EBBA: An Enhanced Binary Bat Algorithm Integrated with Chaos Theory and Lévy Flight for Feature SelectionJinghui Feng0Haopeng Kuang1Lihua Zhang2Academy for Engineering & Technology, Fudan University, Shanghai 200433, ChinaAcademy for Engineering & Technology, Fudan University, Shanghai 200433, ChinaAcademy for Engineering & Technology, Fudan University, Shanghai 200433, ChinaFeature selection can efficiently improve classification accuracy and reduce the dimension of datasets. However, feature selection is a challenging and complex task that requires a high-performance optimization algorithm. In this paper, we propose an enhanced binary bat algorithm (EBBA) which is originated from the conventional binary bat algorithm (BBA) as the learning algorithm in a wrapper-based feature selection model. First, we model the feature selection problem and then transfer it as a fitness function. Then, we propose an EBBA for solving the feature selection problem. In EBBA, we introduce the Lévy flight-based global search method, population diversity boosting method and chaos-based loudness method to improve the BA and make it more applicable to feature selection problems. Finally, the simulations are conducted to evaluate the proposed EBBA and the simulation results demonstrate that the proposed EBBA outmatches other comparison benchmarks. Moreover, we also illustrate the effectiveness of the proposed improved factors by tests.https://www.mdpi.com/1999-5903/14/6/178feature selectionbat algorithmoptimizationchaos theoryLévy flight |
| spellingShingle | Jinghui Feng Haopeng Kuang Lihua Zhang EBBA: An Enhanced Binary Bat Algorithm Integrated with Chaos Theory and Lévy Flight for Feature Selection Future Internet feature selection bat algorithm optimization chaos theory Lévy flight |
| title | EBBA: An Enhanced Binary Bat Algorithm Integrated with Chaos Theory and Lévy Flight for Feature Selection |
| title_full | EBBA: An Enhanced Binary Bat Algorithm Integrated with Chaos Theory and Lévy Flight for Feature Selection |
| title_fullStr | EBBA: An Enhanced Binary Bat Algorithm Integrated with Chaos Theory and Lévy Flight for Feature Selection |
| title_full_unstemmed | EBBA: An Enhanced Binary Bat Algorithm Integrated with Chaos Theory and Lévy Flight for Feature Selection |
| title_short | EBBA: An Enhanced Binary Bat Algorithm Integrated with Chaos Theory and Lévy Flight for Feature Selection |
| title_sort | ebba an enhanced binary bat algorithm integrated with chaos theory and levy flight for feature selection |
| topic | feature selection bat algorithm optimization chaos theory Lévy flight |
| url | https://www.mdpi.com/1999-5903/14/6/178 |
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