基于KFCM改进SVM的学生学业预警系统设计

Design of Early Warning System for Student’s Poor Academic Performance Based on SVM Improved by KFCM

针对高校学业困难学生过程化管理的预警需求,通过学生的入学成绩、学期成绩、一卡通应用数据、早操考勤和学籍处理等状态数据,基于大数据处理思想,提出用核函数的模糊均值聚类(KFCM)改进支持向量机(SVM)数据决策算法,开展学生学业预警决策研究,为教师或学生个人及早采取有效干预措施提供技术支持。经仿真对比分析,本文改进算法相比传统SVM、BP神经网络、遗传算法优化支持向量机(GA-SVM)数据决策算法,在解决学生学业预警领域具有较大优势,有一定推广应用价值。

In view of the warning requirement for university students who have academic difficulty,through the student admission scores,semester grades,IC card application,morning exercises attendance and status data processing and other state data,based on large data processing,this paper puts forward KFCM( kernelized fuzzy C-means) to improve the SVM( support vector machine) data decision algorithm,and to develop students’ academic warning decision research. Teachers and students can take effective intervention measures to provide technical support. Improved Algorithm,through simulation analysis,is compared with the traditional data decision algorithms by using SVM,BP neural network,and GA-SVM,the proposed algorithm has greater advantages in solving the students’ academic warning area,and has the certain application value.