Performance prediction based on fuzzy clustering and support vector regression

SHEN Hang-jie; JU Sheng-gen; SUN Jie-ping

doi:10.3969/j.issn.1000-5641.2019.05.005

Issue 5

Dec. 2019

Turn off MathJax

Article Contents

Article Navigation > Journal of East China Normal University (Natural Sciences) > 2019 > (5): 66-73, 84

SHEN Hang-jie, JU Sheng-gen, SUN Jie-ping. Performance prediction based on fuzzy clustering and support vector regression[J]. Journal of East China Normal University (Natural Sciences), 2019, (5): 66-73, 84. doi: 10.3969/j.issn.1000-5641.2019.05.005

Citation:

SHEN Hang-jie, JU Sheng-gen, SUN Jie-ping. Performance prediction based on fuzzy clustering and support vector regression[J]. Journal of East China Normal University (Natural Sciences), 2019, (5): 66-73, 84. doi: 10.3969/j.issn.1000-5641.2019.05.005

Citation:

PDF( 603 KB)

Performance prediction based on fuzzy clustering and support vector regression

doi: 10.3969/j.issn.1000-5641.2019.05.005

College of Computer Science, Sichuan University, Chengdu 610065, China

Received Date: 2019-07-28
Publish Date: 2019-09-25

Abstract

Abstract

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.
- fuzzy clustering,
- support vector regression,
- prediction,
- educational data mining

FullText(HTML)

References(12)

References

[1]	吕红胤, 连德富, 聂敏, 等.大数据引领教育未来:从成绩预测谈起[J].大数据, 2015, 1(4):118-121.
[2]	BORKAR S, RAJESWARI K. Attributes selection for predicting students' academic performance using education data mining and artificial neural network[J]. International Journal of Computer Applications, 2014, 86(10):25-29. doi: 10.5120/15022-3310
[3]	LAN A S, WATERS A E, STUDER C, et al. Sparse factor analysis for learning and content analytics[J]. Journal of Machine Learning Research, 2013, 15(1):1959-2008. http://d.old.wanfangdata.com.cn/OAPaper/oai_arXiv.org_1303.5685
[4]	张嘉, 张晖, 赵旭剑, 等.规则半自动学习的概率软逻辑推理模型[J].计算机应用, 2018, 38(11):98-103. http://d.old.wanfangdata.com.cn/Periodical/jsjyy201811017
[5]	薛颖, 沙秀艳.基于改进模糊聚类算法的灰色预测模型[J].统计与决策, 2017(9):29-32. http://d.old.wanfangdata.com.cn/Periodical/tjyjc201709006
[6]	文传军, 詹永照.基于样本模糊隶属度归n化约束的松弛模糊C均值聚类算法[J].科学技术与工程, 2017, 17(36):96-104. doi: 10.3969/j.issn.1671-1815.2017.36.015
[7]	赵琦, 孙泽斌, 冯文全, 等.一种基于支持向量回归的建模方法[J].北京航空航天大学学报, 2017, 43(2):352-359 http://d.old.wanfangdata.com.cn/Periodical/bjhkhtdxxb201702018
[8]	张麒增, 戴翰波.基于数据预处理技术的学生成绩预测模型研究[J].湖北大学学报(自然科学版), 2019, 41(1):106-113. http://d.old.wanfangdata.com.cn/Periodical/hbdxxb201901019
[9]	孙毅, 刘仁云, 王松, 等.基于多元线性回归模型的考试成绩评价与预测[J].吉林大学学报(信息科学版), 2013, 31(4):404-408. doi: 10.3969/j.issn.1671-5896.2013.04.013
[10]	陈岷.因子分析和神经网络相融合的体育成绩预测模型[J].现代电子技术, 2017(5):138-141. http://d.old.wanfangdata.com.cn/Periodical/xddzjs201705033
[11]	NÚÑEZ J C, SUÁREZ N, ROSÁRIO P, et al. Relationships between perceived parental involvement in homework, student homework behaviors, and academic achievement:Differences among elementary, junior high, and high school students[J]. Metacognition and Learning, 2015, 10(3):375-406. doi: 10.1007/s11409-015-9135-5
[12]	BUNKAR K, SINGH U K, PANDYA B, et al. Data mining: Prediction for performance improvement of graduate students using classification[C]//IEEE 2012 Ninth International Conference on Wireless and Optical Communications Networks (WOCN). New York: IEEE, 2012: 1-5.