Forecasting outbound student mobility: A machine learning approach.

ABSTRACT:

Background

A 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.

Methods

To 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.

Results

The proposed approach

SUBMITTER: Yang S 

PROVIDER: S-EPMC7470415 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature