Classifier Selection and Ensemble Model for Multi-class Imbalance Learning in Education Grants Prediction

ABSTRACT

Ensemble learning combines base classifiers to improve the performance of the models and obtains a higher classification accuracy than a single classifier. We propose a multi-classification method to predict the level of grant for each college student based on feature integration and ensemble learning. It extracted from expense, score, in/out dormitory, book loan conditions of 10885 students’ daily behavior data and constructed a 21-dimensional feature. The ensemble learning method integrated gradient boosting decision tree, random forest, AdaBoost, and Support Vector Machine classifiers for college grant classification. The proposed method is evaluated with 10885 students set and experiments show that the proposed method has an average accuracy of 0.954 5 and can be used as an effective means of assisting decision-making for college student grants.

Additional information

Funding

This work was supported in part by the Natural Science Foundation of Shaanxi Province (No.2019JLZ-08), in part by the Natural Science Foundation of Shaanxi Province (No.2019JLM-10), in part by the Natural Science Foundation of Shaanxi Province (No.2019JM-348), in part by the National Natural Science Foundation of China under Grant No.61902311, in part by the Scientific and Technology Program funded by Beilin District, Xi’an City (No.GX1926);National Natural Science Foundation of China [No.61902311];