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.

摘要

集成学习结合了基础分类器，以提高模型的性能，并获得比单个分类器更高的分类精度。我们提出了一种基于特征集成和集成学习的多分类方法来预测每个大学生的资助水平。它从费用，分数，进/出宿舍，预定10885名学生日常行为数据的借阅条件中提取出来，并构建了21维特征。集成学习方法将梯度提升决策树，随机森林，AdaBoost和支持向量机分类器集成在一起，用于大学资助分类。该方法在10885名学生中得到了评估，实验表明该方法的平均准确度为0。