基于KFCM改进SVM的学生学业预警系统设计
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摘要
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.
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.
引文
[1]曾明星,周清平,蔡国民,等.软件开发类课程翻转课堂教学模式研究[J].实验室研究与探索,2014,33(2):203-209.
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[9]谢娟英,高红超.基于统计相关性与K-means的区分基因子集选择算法[J].软件学报,2014,25(9):2050-2075.
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[11] Cristianini N,Shawetaylor J. An introduction to support vector machines and other kernel-based learning methods[J]. Kybernetes,2001,32(1):1-28.
[12]刘景发,刘思妤.带静不平衡约束的矩形装填问题的启发式算法[J].软件学报,2018,29(2):283-298.
[13] Miyamoto S. Different objective functions in fuzzy c-means algorithms and kernel-based clustering[J]. International Journal of Fuzzy Systems,2011,13(2):89-97.
[14] Rubat M,Holte R C,Matwin S. Machine learning for the detection of oil spills in sutellite radar Images[J]. Machine Learning,1998,30(2-3):195-215.
[15] Rubat M,Holte R C,Matwin S. Learning when negative examples abound[J]. Lecture Note in Computer Science,1997:146-153.
[2] James C L,Graham S. An empirical study of students on academic probation[J]. Journal of The First-Year Experience&Students in Transition,2010,22:71-91.
[3] Demetriou C. The attribution theory of learning and advising students on academic probation[J]. Nacada Journal,2011,31(2):16-21.
[4]丁宏.高校学籍管理预警机制研究与实践——基于黑龙江省某高校学业预警工作的调研分析[J].黑龙江教育(高教研究与评估),2014(7):67-68.
[5]李平,毛昌杰,徐进.开展国家级虚拟仿真实验教学中心建设提高高校实验教学信息化水平[J].实验室研究与探索,2013,32(11):5-8.
[6] Demetriou C. The attribution theory of learning and advising students on academic probation.[J]. Nacada Journal,2011,31(2):16-21.
[7]倪红军.基于Android平台的消息推送研究与实现[J].实验室研究与探索,2014,33(5):96-100.
[8]宫锋.数据挖掘在高校学生学业预警中的应用[J].电子技术与软件工程,2017(4):202-203.
[9]谢娟英,高红超.基于统计相关性与K-means的区分基因子集选择算法[J].软件学报,2014,25(9):2050-2075.
[10]谢娟英,谢维信.基于特征子集区分度与支持向量机的特征选择算法[J].计算机学报,2014,37(8):1704-1718.
[11] Cristianini N,Shawetaylor J. An introduction to support vector machines and other kernel-based learning methods[J]. Kybernetes,2001,32(1):1-28.
[12]刘景发,刘思妤.带静不平衡约束的矩形装填问题的启发式算法[J].软件学报,2018,29(2):283-298.
[13] Miyamoto S. Different objective functions in fuzzy c-means algorithms and kernel-based clustering[J]. International Journal of Fuzzy Systems,2011,13(2):89-97.
[14] Rubat M,Holte R C,Matwin S. Machine learning for the detection of oil spills in sutellite radar Images[J]. Machine Learning,1998,30(2-3):195-215.
[15] Rubat M,Holte R C,Matwin S. Learning when negative examples abound[J]. Lecture Note in Computer Science,1997:146-153.