基于贝叶斯分类器的简历筛选模型

Screening of Resumes Based on Bayesian Classifier

现代社会网络招聘兴起,为社会、企业节省了不少物力、人力、财力,但如何快速、简捷地从众多的电子简历中找出符合要求的简历又是一个让人头疼的问题。文中在贝叶斯网络的基础上,分别从朴素贝叶斯分类器和TAN分类器角度,构建一个基于贝叶斯分类器的电子简历筛选模型,并通过实验验证该模型对电子简历进行分类时的准确率和查全率,且引入一个新的评价指标f同时考虑准确率和查全率,得出没有属性变量相互独立限制的TAN分类器具有较好的分类效果的结论。

To be on the upgrade of network recruitment in the modem society,it saves a lot of material,human and financial resources for the community. But it is a problem to find qualified resumes from electronic resumes quickly and simply. In this paper, construct the screening of electronic resumes on Bayesian network from the Naive Bayesian classifier and tree augmented Naive Bayesian classifier. Then discuss the precision and recall of Bayesian classifier model through experiment, and bring a new evaluation index f. Finally, TAN classifier has good classification results.