摘要
为提高高校毕业生就业预测的准确度,引入改进贝叶斯模型,提出一种全新的毕业生就业预测方法。首先,建立高校毕业生就业影响因素分析模型,对毕业生就业数据进行预处理,获取待预测属性与数据集中其他属性之间的相关性;其次,利用改进贝叶斯模型提高就业确信程度,对高校毕业生就业情况作出预测;最后,进行实验对比分析。实验结果表明,该方法的F1值均在0.8以上,预测结果的准确率和召回率得到了显著提升。
In order to improve the accuracy of college graduates'employment prediction,an improved Bayesian model is introduced and a new method of graduates'employment prediction is proposed.Firstly,establish an analysis model of the factors affecting the employment of college graduates,preprocess the employment data of graduates,and obtain the correlation between the attributes to be predicted and other attributes in the data set.Secondly,the improved Bayesian model is used to improve the degree of confidence in employment and predict the employment situation of college graduates.Finally,the experiment is compared and analyzed.The experimental results show that the FI value of the method is above 0.8,and the accuracy and recall rate of the prediction results have been significantly improved.
作者
王娜
张吉
王金鑫
WANG Na;ZHANG Ji;WANG Jinxin(School of Management,Changchun University of Architecture and Engineer,Changchun Jilin 130607,China;School of Electrical Engineering,Changchun University of Architecture and Engineer,Changchun Jilin 130607,China)
出处
《信息与电脑》
2023年第5期242-244,共3页
Information & Computer
基金
2021年度吉林省高教科研重点课题“吉林省大学生留省就业问题研究”(项目编号:JGJX2021C78)。
关键词
改进贝叶斯模型
预测
就业
毕业生
improved Bayesian model
forecast
obtain employment
graduate
作者简介
王娜(1982-),女,吉林长春人,硕士研究生,副教授。研究方向:大学生就业;通信作者:张吉(1978-),男,吉林长春人,硕士研究生,助教。研究方向:社会工作。
