Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Wei, Yan, Rao, Xili, Fu, Yinjun, Song, Li, Chen, Huiling, Li, Junhong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
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
_version_ 1785133005080100864
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
work_keys_str_mv AT weiyan machinelearningpredictionmodelbasedonenhancedbatalgorithmandsupportvectormachineforslowemploymentprediction
AT raoxili machinelearningpredictionmodelbasedonenhancedbatalgorithmandsupportvectormachineforslowemploymentprediction
AT fuyinjun machinelearningpredictionmodelbasedonenhancedbatalgorithmandsupportvectormachineforslowemploymentprediction
AT songli machinelearningpredictionmodelbasedonenhancedbatalgorithmandsupportvectormachineforslowemploymentprediction
AT chenhuiling machinelearningpredictionmodelbasedonenhancedbatalgorithmandsupportvectormachineforslowemploymentprediction
AT lijunhong machinelearningpredictionmodelbasedonenhancedbatalgorithmandsupportvectormachineforslowemploymentprediction