摘要
贝叶斯网络通过确定变量结点网络结构和参数学习来进行网络模型的概率推理,在样本数据不是过大的条件下能得到较精确的预测结果.从高校统一标准化的各数据平台中选取学生行为数据作为训练样本构建贝叶斯网络并进行参数学习得到推理模型,进而对高校学生的贫困程度进行预测,可以得出预测结果与实际样本对比没有显著差异,实现用数据分析精确判定高校学生的贫困程度水平.
Bayesian network performs probabilistic inference for network model by determining variable node network structure and parameter learning,under the condition of sample data is not too big,an accurate prediction results can be obtained.The training sample data are selected from each data platform for the standardization of college and university student behavior,which is used to build a Bayesian network and to learn the parameters by the network to get the inference model,and then the poverty status of college students is predicted by the model.The predict results show that there are no significant differences between the predict results and the actual samples.Thus the poverty level of college student can be accurately determined by data analysis.
作者
李斌
王卫星
胡屹峰
王萍
LI Bin;WANG Wei-Xing;HU Yi-Feng;WANG Ping(Modern Education Technology Center, School of Information Engineering, Henan University of Science and Technology, Sanmenxia 472000, China)
出处
《计算机系统应用》
2019年第1期262-268,共7页
Computer Systems & Applications
基金
河南省教育厅高等教育教学改革与实践项目(2017SJGLX636)
河南省社科联
河南省经团联2018年度调研课题(SKL-2018-665)~~
