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Forecasting outbound student mobility: A machine learning approach
- Source :
- PLoS ONE, Vol 15, Iss 9, p e0238129 (2020), PLoS ONE
- Publication Year :
- 2020
- Publisher :
- Public Library of Science (PLoS), 2020.
-
Abstract
- BackgroundA country's ability to become a prominent knowledge economy is tied closely to its ability to acquire skilled people who can compete internationally while resolving challenges of the future. To equip students with competence that can only by gained by being immersed in a foreign environment, outbound mobility is vital.MethodsTo analyze outbound student mobility in Taiwan using time series methods, this study aims to propose a hybrid approach FSDESVR which combines feature selection (FS) and support vector regression (SVR) with differential evolution (DE). FS and a DE algorithm were used for selecting reliable input features and determining the optimal initial parameters of SVR, respectively, to achieve high forecast accuracy.ResultsThe proposed approach was examined using a dataset of outbound Taiwanese student mobility to ten countries between 1998 and 2018. Without the requirements of any special conditions for the proprieties of the objective function and constraints, the FSDESVR model retained the advantage of FS, SVR, and DE. A comparison of the FSDESVR model and other forecasting models revealed that FSDESVR provided the lowest mean absolute percentage error (MAPE) and root mean square error (RMSE) results for all the analyzed nations. The experimental results indicate that FSDESVR achieved higher forecasting accuracy than the compared models from the literature.ConclusionWith the recognition of outbound values, key findings of Taiwanese outbound student mobility, and accurate application of the FSDESVR model, education administration units are exposed to a more in-depth view of future student mobility, which enables the implement of a more accurate education curriculum.
- Subjects :
- Databases, Factual
Computer science
Evolutionary algorithm
Social Sciences
02 engineering and technology
computer.software_genre
Geographical Locations
Machine Learning
Mathematical and Statistical Techniques
Learning and Memory
Japan
0202 electrical engineering, electronic engineering, information engineering
Psychology
0303 health sciences
Multidisciplinary
Applied Mathematics
Simulation and Modeling
Statistics
Europe
Mean absolute percentage error
Differential evolution
Physical Sciences
Medicine
020201 artificial intelligence & image processing
Algorithms
Research Article
Asia
Mean squared error
Science
Oceania
Taiwan
Feature selection
Machine learning
Research and Analysis Methods
03 medical and health sciences
Human Learning
Computational Techniques
Learning
Humans
European Union
Statistical Methods
Students
030304 developmental biology
business.industry
Cognitive Psychology
Australia
Biology and Life Sciences
United States
United Kingdom
Support vector machine
People and Places
North America
Cognitive Science
Artificial intelligence
Evolutionary Algorithms
business
Evolutionary Computation
computer
Mathematics
Forecasting
Neuroscience
Subjects
Details
- Language :
- English
- ISSN :
- 19326203
- Volume :
- 15
- Issue :
- 9
- Database :
- OpenAIRE
- Journal :
- PLoS ONE
- Accession number :
- edsair.doi.dedup.....781f083457763fdaeb50e0c30c1e8f12