Tool for Predicting College Student Career Decisions: An Enhanced Support Vector Machine Framework
The goal of this research is to offer an effective intelligent model for forecasting college students’ career decisions in order to give a useful reference for career decisions and policy formation by relevant departments. The suggested prediction model is mainly based on a support vector machine (S...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/9/4776 |
| _version_ | 1797505494329327616 |
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| author | Zhuang Wang Guoxi Liang Huiling Chen |
| author_facet | Zhuang Wang Guoxi Liang Huiling Chen |
| author_sort | Zhuang Wang |
| collection | DOAJ |
| description | The goal of this research is to offer an effective intelligent model for forecasting college students’ career decisions in order to give a useful reference for career decisions and policy formation by relevant departments. The suggested prediction model is mainly based on a support vector machine (SVM) that has been modified using an enhanced butterfly optimization approach with a communication mechanism and Gaussian bare-bones mechanism (CBBOA). To get a better set of parameters and feature subsets, first, we added a communication mechanism to BOA to improve its global search capability and balance exploration and exploitation trends. Then, Gaussian bare-bones was added to increase the population diversity of BOA and its ability to jump out of the local optimum. The optimal SVM model (CBBOA-SVM) was then developed to predict the career decisions of college students based on the obtained parameters and feature subsets that are already optimized by CBBOA. In order to verify the effectiveness of CBBOA, we compared it with some advanced algorithms on all benchmark functions of CEC2014. Simulation results demonstrated that the performance of CBBOA is indeed more comprehensive. Meanwhile, comparisons between CBBOA-SVM and other machine learning approaches for career decision prediction were carried out, and the findings demonstrate that the provided CBBOA-SVM has better classification and more stable performance. As a result, it is plausible to conclude that the CBBOA-SVM is capable of being an effective tool for predicting college student career decisions. |
| first_indexed | 2024-03-10T04:19:23Z |
| format | Article |
| id | doaj.art-dbf7d971c78a4b0cac84cc401363c9e0 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T04:19:23Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-dbf7d971c78a4b0cac84cc401363c9e02023-11-23T07:53:50ZengMDPI AGApplied Sciences2076-34172022-05-01129477610.3390/app12094776Tool for Predicting College Student Career Decisions: An Enhanced Support Vector Machine FrameworkZhuang Wang0Guoxi Liang1Huiling Chen2The Student Affairs Office, Wenzhou University, Wenzhou 325035, ChinaDepartment of Information Technology, Wenzhou Polytechnic, Wenzhou 325035, ChinaCollege of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, ChinaThe goal of this research is to offer an effective intelligent model for forecasting college students’ career decisions in order to give a useful reference for career decisions and policy formation by relevant departments. The suggested prediction model is mainly based on a support vector machine (SVM) that has been modified using an enhanced butterfly optimization approach with a communication mechanism and Gaussian bare-bones mechanism (CBBOA). To get a better set of parameters and feature subsets, first, we added a communication mechanism to BOA to improve its global search capability and balance exploration and exploitation trends. Then, Gaussian bare-bones was added to increase the population diversity of BOA and its ability to jump out of the local optimum. The optimal SVM model (CBBOA-SVM) was then developed to predict the career decisions of college students based on the obtained parameters and feature subsets that are already optimized by CBBOA. In order to verify the effectiveness of CBBOA, we compared it with some advanced algorithms on all benchmark functions of CEC2014. Simulation results demonstrated that the performance of CBBOA is indeed more comprehensive. Meanwhile, comparisons between CBBOA-SVM and other machine learning approaches for career decision prediction were carried out, and the findings demonstrate that the provided CBBOA-SVM has better classification and more stable performance. As a result, it is plausible to conclude that the CBBOA-SVM is capable of being an effective tool for predicting college student career decisions.https://www.mdpi.com/2076-3417/12/9/4776self-determination theoryglobal optimizationswarm intelligencecollege student career decisionssupport vector machine |
| spellingShingle | Zhuang Wang Guoxi Liang Huiling Chen Tool for Predicting College Student Career Decisions: An Enhanced Support Vector Machine Framework Applied Sciences self-determination theory global optimization swarm intelligence college student career decisions support vector machine |
| title | Tool for Predicting College Student Career Decisions: An Enhanced Support Vector Machine Framework |
| title_full | Tool for Predicting College Student Career Decisions: An Enhanced Support Vector Machine Framework |
| title_fullStr | Tool for Predicting College Student Career Decisions: An Enhanced Support Vector Machine Framework |
| title_full_unstemmed | Tool for Predicting College Student Career Decisions: An Enhanced Support Vector Machine Framework |
| title_short | Tool for Predicting College Student Career Decisions: An Enhanced Support Vector Machine Framework |
| title_sort | tool for predicting college student career decisions an enhanced support vector machine framework |
| topic | self-determination theory global optimization swarm intelligence college student career decisions support vector machine |
| url | https://www.mdpi.com/2076-3417/12/9/4776 |
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