大学生就业类别与学业表现的关系模型  

Model for Relationship Between College Student Employment Category and Academic Performance

作　　者：朱忠旭[1] 

机构地区：[1]安徽工贸职业技术学院计算机信息工程系,安徽淮南232007

出　　处：《工业控制计算机》2021年第11期108-110,共3页Industrial Control Computer

基　　金：安徽省高校自然科学研究重点项目“贝叶斯网络在大学生就业能力培养中的应用研究”(KJ2019A1054)。

摘　　要：对大学生所学课程进行分组和投影得到一组隐藏变量,这些隐藏变量之间的相关性很小,可以视为相互独立,将它们作为就业类别结点的子结点,由此得到一个三层次的树型网络结构,网络参数使用EM算法学习获得。通过和朴素贝叶斯形式的就业模型的对比发现,该模型就业预测的准确率高于朴素贝叶斯网,而且受就业样本数量的影响较小。Grouping and projection courses learned by college students yields a set of hidden variables with little correlation.These hidden variables can be regarded as independent of each other,so can be taken as sub-nodes of employment category node,resulting in a three-level tree-based network structure.The parameters can be learned using the EM algorithm.The comparison with the employment model in the Naive-Bayesian form reveals that the employment prediction is higher than the Naive-Bayesian network and is less affected by the number of employment samples.

关 键 词：朴素贝叶斯网 就业能力 就业预测 

分 类 号：G647.38[文化科学—高等教育学]