基于增强特征判别性的典型相关分析和分类集成的助学金预测方法

针对高校资助管理办法效率低下、工作量大等问题，提出一种增强特征判别性的典型相关分析（EN-DCCA）方法，并结合分类集成方法实现高校学生助学金预测。将学生在校多维度数据划分为两个不同视图，已有的各种多视图判别典型相关分析算法没有综合考虑视图类别之间的相关性和视图组合特征的判别性两者因素。EN-DCCA的优化目标在最大化类内相关的同时最小化类间相关，并且考虑了视图组合特征的判别性，进一步强化了属性的判别性能，更有利于分类预测。高校学生助学金预测的实现过程：首先，根据学生生活行为和学习表现将数据预处理为两个不同视图，然后用EN-DCCA方法对这两个视图数据进行特征学习，最后用分类集成方法完成预测。在真实的数据集上进行实验，所提方法的预测准确率达到90.01%，较增强视图组合特征判别性的典型相关分析（CECCA）的集成方法提高了2个百分点，实验结果表明，所提方法能有效实现高校助学金预测。

Stipend prediction based on enhanced-discriminative canonical correlations analysis and classification ensemble

For low efficiency and high workload of higher education institution's stipend management, an algorithm of Enhanced-Discriminative Canonical Correlations Analysis (EN-DCCA) was proposed, and the method of classification ensemble was combined to predict the stipend of undergraduates. The multi-dimensional data of undergraduates at school were divided into two different views. The existing multi-view discriminative canonical correlation analysis algorithms do not consider both the correlation between view categories and the discrimination of view's combination features. The optimization goal of EN-DCCA was to minimize inter-class correlation while maximizing intra-class correlation and considered the discrimination of view's combination features, which further enhanced the performance of attribute identification and was more conducive to classification prediction. The process of undergraduates' stipend prediction is as follows:firstly, according to undergraduates' learning behavior and living behavior at school, the data was preprocessed as two different views. Then, the two views were learned by EN-DCCA. Finally, the classification ensemble was used to complete the prediction. Experimented on a real data set, the prediction accuracy of the proposed method reached 90.01%, which was 2 percentage points higher than that of Combined-feature-discriminability Enhanced Canonical Correlation Analysis (CECCA) method. The experimental results show that the proposed method can effectively achieve the stipend prediction for higher education institutions.