Employment management system for universities based on improved decision tree

Abstract

With the popularization of higher education, the number of students in colleges and universities is increasing, and how to timely cope with the various problems faced by students in employment has become a major problem faced by teachers in colleges and universities. Due to the low utilization rate of student information by the traditional employment management of college graduates, the quality of employment guidance services is not high. Therefore, to solve this problem, this study proposes a simplified, improved Iterative Dichotomizer 3 (ID3) based on the correlation coefficient, and the algorithm improves the information gain function and simplifies the information entropy formula. The experimental results show that the simplified modified ID3 based on correlation coefficients converges faster than the other two algorithms, starting to converge after only 17 iterations; the loss value is also smaller than the other algorithms, at around 0.12. Its minimum accuracy, precision, recall, and F1-measure for employment status prediction were 86.4, 76.8, 72.8, and 0.82%, respectively, all higher than the rest of the algorithms. The time complexity at a sample size of 80 is only 32 ms, which is lower than the rest of the algorithms. It can be seen that the simplified and improved ID3 based on correlation coefficients can accurately and efficiently perform predictive analysis of graduates’ employment status. The university employment management system proposed in the study has achieved efficient deep utilization of graduate information through ID3, providing assistance to university employment decision-makers and reference for employment guidance for university graduates.

Keywords

• decision trees
• data mining
• employment management
• iterative dichotomizer 3