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Machine learning prediction model based on enhanced bat algorithm and support vector machine for slow employment prediction
The employment of college students is an important issue that affects national development and social stability. In recent years, the increase in the number of graduates, the pressure of employment, and the epidemic have made the phenomenon of ’slow employment’ increasingly prominent, becoming an ur...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10635481/ https://www.ncbi.nlm.nih.gov/pubmed/37943766 http://dx.doi.org/10.1371/journal.pone.0294114 |
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author | Wei, Yan Rao, Xili Fu, Yinjun Song, Li Chen, Huiling Li, Junhong |
author_facet | Wei, Yan Rao, Xili Fu, Yinjun Song, Li Chen, Huiling Li, Junhong |
author_sort | Wei, Yan |
collection | PubMed |
description | The employment of college students is an important issue that affects national development and social stability. In recent years, the increase in the number of graduates, the pressure of employment, and the epidemic have made the phenomenon of ’slow employment’ increasingly prominent, becoming an urgent problem to be solved. Data mining and machine learning methods are used to analyze and predict the employment prospects for graduates and provide effective employment guidance and services for universities, governments, and graduates. It is a feasible solution to alleviate the problem of ’slow employment’ of graduates. Therefore, this study proposed a feature selection prediction model (bGEBA-SVM) based on an improved bat algorithm and support vector machine by extracting 1694 college graduates from 2022 classes in Zhejiang Province. To improve the search efficiency and accuracy of the optimal feature subset, this paper proposed an enhanced bat algorithm based on the Gaussian distribution-based and elimination strategies for optimizing the feature set. The training data were input to the support vector machine for prediction. The proposed method is experimented by comparing it with peers, well-known machine learning models on the IEEE CEC2017 benchmark functions, public datasets, and graduate employment prediction dataset. The experimental results show that bGEBA-SVM can obtain higher prediction Accuracy, which can reach 93.86%. In addition, further education, student leader experience, family situation, career planning, and employment structure are more relevant characteristics that affect employment outcomes. In summary, bGEBA-SVM can be regarded as an employment prediction model with strong performance and high interpretability. |
format | Online Article Text |
id | pubmed-10635481 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-106354812023-11-10 Machine learning prediction model based on enhanced bat algorithm and support vector machine for slow employment prediction Wei, Yan Rao, Xili Fu, Yinjun Song, Li Chen, Huiling Li, Junhong PLoS One Research Article The employment of college students is an important issue that affects national development and social stability. In recent years, the increase in the number of graduates, the pressure of employment, and the epidemic have made the phenomenon of ’slow employment’ increasingly prominent, becoming an urgent problem to be solved. Data mining and machine learning methods are used to analyze and predict the employment prospects for graduates and provide effective employment guidance and services for universities, governments, and graduates. It is a feasible solution to alleviate the problem of ’slow employment’ of graduates. Therefore, this study proposed a feature selection prediction model (bGEBA-SVM) based on an improved bat algorithm and support vector machine by extracting 1694 college graduates from 2022 classes in Zhejiang Province. To improve the search efficiency and accuracy of the optimal feature subset, this paper proposed an enhanced bat algorithm based on the Gaussian distribution-based and elimination strategies for optimizing the feature set. The training data were input to the support vector machine for prediction. The proposed method is experimented by comparing it with peers, well-known machine learning models on the IEEE CEC2017 benchmark functions, public datasets, and graduate employment prediction dataset. The experimental results show that bGEBA-SVM can obtain higher prediction Accuracy, which can reach 93.86%. In addition, further education, student leader experience, family situation, career planning, and employment structure are more relevant characteristics that affect employment outcomes. In summary, bGEBA-SVM can be regarded as an employment prediction model with strong performance and high interpretability. Public Library of Science 2023-11-09 /pmc/articles/PMC10635481/ /pubmed/37943766 http://dx.doi.org/10.1371/journal.pone.0294114 Text en © 2023 Wei et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Wei, Yan Rao, Xili Fu, Yinjun Song, Li Chen, Huiling Li, Junhong Machine learning prediction model based on enhanced bat algorithm and support vector machine for slow employment prediction |
title | Machine learning prediction model based on enhanced bat algorithm and support vector machine for slow employment prediction |
title_full | Machine learning prediction model based on enhanced bat algorithm and support vector machine for slow employment prediction |
title_fullStr | Machine learning prediction model based on enhanced bat algorithm and support vector machine for slow employment prediction |
title_full_unstemmed | Machine learning prediction model based on enhanced bat algorithm and support vector machine for slow employment prediction |
title_short | Machine learning prediction model based on enhanced bat algorithm and support vector machine for slow employment prediction |
title_sort | machine learning prediction model based on enhanced bat algorithm and support vector machine for slow employment prediction |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10635481/ https://www.ncbi.nlm.nih.gov/pubmed/37943766 http://dx.doi.org/10.1371/journal.pone.0294114 |
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