Improved RIME algorithm: CEC performance analysis and feature selection experiment of Sino foreign cooperative data sets
Abstract In recent years, with the rapid development of higher education, China has actively constructed an evaluation framework of education quality. As an important part of higher education, Sino foreign cooperation in running schools plays an important role in the development of higher education....
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-04-01
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| Series: | Journal of Big Data |
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| Online Access: | https://doi.org/10.1186/s40537-025-01145-2 |
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| author | Wei Zhu Yuxi Hu Lei Liu Zhennao Cai Huiling Chen Guoxi Liang |
| author_facet | Wei Zhu Yuxi Hu Lei Liu Zhennao Cai Huiling Chen Guoxi Liang |
| author_sort | Wei Zhu |
| collection | DOAJ |
| description | Abstract In recent years, with the rapid development of higher education, China has actively constructed an evaluation framework of education quality. As an important part of higher education, Sino foreign cooperation in running schools plays an important role in the development of higher education. The newly released evaluation criteria for Sino foreign cooperative education cover a series of influencing factors. However, objectively determining which of these factors are crucial for the success of Sino foreign cooperative education is essential for strengthening its future development. To address this challenge, we propose an adaptive hunting mechanism that utilizes the latest RIME algorithm and is enhanced through a criss-crossing mechanism, as well as a new ACRIME algorithm. We conducted a comparative analysis of ACRIME, the original RIME, and several other highly acclaimed improved algorithms. The results indicate that ACRIME exhibits excellent performance in multiple benchmark tests. Subsequently, we applied the ACRIME algorithm to cluster the dataset of Sino foreign cooperative education, and then used the binary version of ACRIME (bACRIME) for feature selection. In the tenfold cross validation, more than half of the selected features were repeatedly identified, indicating their potential correlation with the development of Sino foreign cooperative education. Therefore, it is necessary to pay more attention to these influential indicators to support future development efforts. |
| format | Article |
| id | doaj-art-834cb71343f04839a7ae2a4a1f42ded1 |
| institution | OA Journals |
| issn | 2196-1115 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Big Data |
| spelling | doaj-art-834cb71343f04839a7ae2a4a1f42ded12025-08-20T02:11:11ZengSpringerOpenJournal of Big Data2196-11152025-04-0112115010.1186/s40537-025-01145-2Improved RIME algorithm: CEC performance analysis and feature selection experiment of Sino foreign cooperative data setsWei Zhu0Yuxi Hu1Lei Liu2Zhennao Cai3Huiling Chen4Guoxi Liang5School of Resources and Safety Engineering, Central South UniversitySchool of Mathematics and Statistics, Central South UniversityCollege of Computer Science, Sichuan UniversityDepartment of Computer Science and Artificial Intelligence, Wenzhou UniversityDepartment of Computer Science and Artificial Intelligence, Wenzhou UniversityDepartment of Artificial Intelligence, Wenzhou PolytechnicAbstract In recent years, with the rapid development of higher education, China has actively constructed an evaluation framework of education quality. As an important part of higher education, Sino foreign cooperation in running schools plays an important role in the development of higher education. The newly released evaluation criteria for Sino foreign cooperative education cover a series of influencing factors. However, objectively determining which of these factors are crucial for the success of Sino foreign cooperative education is essential for strengthening its future development. To address this challenge, we propose an adaptive hunting mechanism that utilizes the latest RIME algorithm and is enhanced through a criss-crossing mechanism, as well as a new ACRIME algorithm. We conducted a comparative analysis of ACRIME, the original RIME, and several other highly acclaimed improved algorithms. The results indicate that ACRIME exhibits excellent performance in multiple benchmark tests. Subsequently, we applied the ACRIME algorithm to cluster the dataset of Sino foreign cooperative education, and then used the binary version of ACRIME (bACRIME) for feature selection. In the tenfold cross validation, more than half of the selected features were repeatedly identified, indicating their potential correlation with the development of Sino foreign cooperative education. Therefore, it is necessary to pay more attention to these influential indicators to support future development efforts.https://doi.org/10.1186/s40537-025-01145-2RIME algorithmAdaptive Hunting mechanismFeature selectionCriss-crossing mechanism |
| spellingShingle | Wei Zhu Yuxi Hu Lei Liu Zhennao Cai Huiling Chen Guoxi Liang Improved RIME algorithm: CEC performance analysis and feature selection experiment of Sino foreign cooperative data sets Journal of Big Data RIME algorithm Adaptive Hunting mechanism Feature selection Criss-crossing mechanism |
| title | Improved RIME algorithm: CEC performance analysis and feature selection experiment of Sino foreign cooperative data sets |
| title_full | Improved RIME algorithm: CEC performance analysis and feature selection experiment of Sino foreign cooperative data sets |
| title_fullStr | Improved RIME algorithm: CEC performance analysis and feature selection experiment of Sino foreign cooperative data sets |
| title_full_unstemmed | Improved RIME algorithm: CEC performance analysis and feature selection experiment of Sino foreign cooperative data sets |
| title_short | Improved RIME algorithm: CEC performance analysis and feature selection experiment of Sino foreign cooperative data sets |
| title_sort | improved rime algorithm cec performance analysis and feature selection experiment of sino foreign cooperative data sets |
| topic | RIME algorithm Adaptive Hunting mechanism Feature selection Criss-crossing mechanism |
| url | https://doi.org/10.1186/s40537-025-01145-2 |
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