现有的成绩预测模型往往过度使用不同类型的属性，导致过于复杂的分数预测方法，或是需要人工参与.为提高学生成绩预测的准确率和可解释性，提出了一种融合模糊聚类和支持向量回归的成绩预测方法.首先引入模糊逻辑来计算隶属度矩阵，根据学生的历史成绩进行聚类，随后对每个聚类簇利用支持向量回归理论对成绩轨迹进行拟合建模.此外，结合学生学习行为等相关属性，对最终的预测结果做调整.在多个基准数据集上进行了实验测试，验证了该方法的有效性.
Existing performance prediction models tend to overuse different types of attributes, leading to either overly complex prediction methods or models that require manual participation. To improve the accuracy and interpretation of student performance prediction, a method based on fuzzy clustering and support vector regression is proposed. Firstly, fuzzy logic is introduced to calculate the membership matrix, and students are clustered according to their past performance. Then, we use Support Vector Regression (SVR) theory to fit and model performance trajectory for each cluster. Lastly, the final prediction results are adjusted in combination with the students' learning behavior and other related attributes. Experimental results on several baseline datasets demonstrate the validity of the proposed approach.
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中国综合性科技类核心期刊(北大核心)