MSBKA: A Multi-Strategy Improved Black-Winged Kite Algorithm for Feature Selection of Natural Disaster Tweets Classification
With the advancement of the Internet, social media platforms have gradually become powerful in spreading crisis-related content. Identifying informative tweets associated with natural disasters is beneficial for the rescue operation. When faced with massive text data, choosing the pivotal features,...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Biomimetics |
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| Online Access: | https://www.mdpi.com/2313-7673/10/1/41 |
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| author | Guangyu Mu Jiaxue Li Zhanhui Liu Jiaxiu Dai Jiayi Qu Xiurong Li |
| author_facet | Guangyu Mu Jiaxue Li Zhanhui Liu Jiaxiu Dai Jiayi Qu Xiurong Li |
| author_sort | Guangyu Mu |
| collection | DOAJ |
| description | With the advancement of the Internet, social media platforms have gradually become powerful in spreading crisis-related content. Identifying informative tweets associated with natural disasters is beneficial for the rescue operation. When faced with massive text data, choosing the pivotal features, reducing the calculation expense, and increasing the model classification performance is a significant challenge. Therefore, this study proposes a multi-strategy improved black-winged kite algorithm (MSBKA) for feature selection of natural disaster tweets classification based on the wrapper method’s principle. Firstly, BKA is improved by utilizing the enhanced Circle mapping, integrating the hierarchical reverse learning, and introducing the Nelder–Mead method. Then, MSBKA is combined with the excellent classifier SVM (RBF kernel function) to construct a hybrid model. Finally, the MSBKA-SVM model performs feature selection and tweet classification tasks. The empirical analysis of the data from four natural disasters shows that the proposed model has achieved an accuracy of 0.8822. Compared with GA, PSO, SSA, and BKA, the accuracy is increased by 4.34%, 2.13%, 2.94%, and 6.35%, respectively. This research proves that the MSBKA-SVM model can play a supporting role in reducing disaster risk. |
| format | Article |
| id | doaj-art-7bcbec489bad401b9c4e309f9e602005 |
| institution | Kabale University |
| issn | 2313-7673 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomimetics |
| spelling | doaj-art-7bcbec489bad401b9c4e309f9e6020052025-01-24T13:24:41ZengMDPI AGBiomimetics2313-76732025-01-011014110.3390/biomimetics10010041MSBKA: A Multi-Strategy Improved Black-Winged Kite Algorithm for Feature Selection of Natural Disaster Tweets ClassificationGuangyu Mu0Jiaxue Li1Zhanhui Liu2Jiaxiu Dai3Jiayi Qu4Xiurong Li5School of Management Science and Information Engineering, Jilin University of Finance and Economics, Changchun 130117, ChinaSchool of Management Science and Information Engineering, Jilin University of Finance and Economics, Changchun 130117, ChinaChangchun Community Official Staff College of Jilin Province, Changchun 130052, ChinaSchool of Management Science and Information Engineering, Jilin University of Finance and Economics, Changchun 130117, ChinaSchool of Management Science and Information Engineering, Jilin University of Finance and Economics, Changchun 130117, ChinaFaculty of Information Technology, Beijing University of Technology, Beijing 100124, ChinaWith the advancement of the Internet, social media platforms have gradually become powerful in spreading crisis-related content. Identifying informative tweets associated with natural disasters is beneficial for the rescue operation. When faced with massive text data, choosing the pivotal features, reducing the calculation expense, and increasing the model classification performance is a significant challenge. Therefore, this study proposes a multi-strategy improved black-winged kite algorithm (MSBKA) for feature selection of natural disaster tweets classification based on the wrapper method’s principle. Firstly, BKA is improved by utilizing the enhanced Circle mapping, integrating the hierarchical reverse learning, and introducing the Nelder–Mead method. Then, MSBKA is combined with the excellent classifier SVM (RBF kernel function) to construct a hybrid model. Finally, the MSBKA-SVM model performs feature selection and tweet classification tasks. The empirical analysis of the data from four natural disasters shows that the proposed model has achieved an accuracy of 0.8822. Compared with GA, PSO, SSA, and BKA, the accuracy is increased by 4.34%, 2.13%, 2.94%, and 6.35%, respectively. This research proves that the MSBKA-SVM model can play a supporting role in reducing disaster risk.https://www.mdpi.com/2313-7673/10/1/41black-winged kite algorithmmachine learningsocial media platformfeature selectionnatural disaster tweetsemergency response |
| spellingShingle | Guangyu Mu Jiaxue Li Zhanhui Liu Jiaxiu Dai Jiayi Qu Xiurong Li MSBKA: A Multi-Strategy Improved Black-Winged Kite Algorithm for Feature Selection of Natural Disaster Tweets Classification Biomimetics black-winged kite algorithm machine learning social media platform feature selection natural disaster tweets emergency response |
| title | MSBKA: A Multi-Strategy Improved Black-Winged Kite Algorithm for Feature Selection of Natural Disaster Tweets Classification |
| title_full | MSBKA: A Multi-Strategy Improved Black-Winged Kite Algorithm for Feature Selection of Natural Disaster Tweets Classification |
| title_fullStr | MSBKA: A Multi-Strategy Improved Black-Winged Kite Algorithm for Feature Selection of Natural Disaster Tweets Classification |
| title_full_unstemmed | MSBKA: A Multi-Strategy Improved Black-Winged Kite Algorithm for Feature Selection of Natural Disaster Tweets Classification |
| title_short | MSBKA: A Multi-Strategy Improved Black-Winged Kite Algorithm for Feature Selection of Natural Disaster Tweets Classification |
| title_sort | msbka a multi strategy improved black winged kite algorithm for feature selection of natural disaster tweets classification |
| topic | black-winged kite algorithm machine learning social media platform feature selection natural disaster tweets emergency response |
| url | https://www.mdpi.com/2313-7673/10/1/41 |
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