Building graduate salary grading prediction model based on deep learning

Abstract

Predicting salary trends of students after employment is vital for help-ing students to develop their career plans. Particularly, salary is not only consid-ered employment information for students to pursue jobs, but also serves as an important indicator for measuring employability and competitiveness of gradu-ates. This paper considers salary prediction as an ordinal regression problem and uses deep learning techniques to build a salary prediction model for determin-ing the relative ordering between different salary grades. Specifically, to solve this problem, the model uses students’ personal information, grades, and family data as input features and employs a multi-output deep neural network to capture the correlation between the salary grades during training. Moreover, the model is pre-trained using a stacked denoising autoencoder and the corresponding weights after pre-training are used as the initial weights of the neural network. To improve the performance of model, dropout and bootstrap aggregation are used. The experimental results are very encouraging. With the predictive salary grades for graduates, school’s researchers can have a clear understanding of salary trends in order to promote student employability and competitiveness.