摘要
针对就业数据中的高维、不平衡、多类别等特点,为了进一步提升决策树方法对高校学生就业预测的准确度,提出了一种基于LightGBM的就业预测模型。首先采用改进的ADASYN综合采样算法,增加数据样本中的少数类,然后采用平衡后的就业数据训练LightGBM算法,并结合贝叶斯模型进行参数寻优,得到最终的就业预测结果,最后对预测模型进行特征分析,度量各个特征对就业的影响程度。通过某高校毕业生的不平衡就业数据集对所提方法的有效性进行验证,与多种不平衡分类方法进行实验对比,证明了本文提出的模型具有更好的预测性能。
In view of the characteristics of high-dimensional,unbalanced and multi-category employment data,in order to further improve the accuracy of decision tree method in the employment prediction of college students,an employment prediction model based on LightGBM is proposed.First the improved ADASYN sampling algorithm is used to increase the minority class in the data sample,and then the employment data after balance is used for training LightGBM algorithm,and Bayesian model is used for parameter optimization to get the final employment prediction.Finally the prediction model is analyzed to measure the influence of each feature on employment.The validity of the proposed method is verified through the data set of unbalanced employment data of college graduates,and compared with various unbalanced classification methods.It is proved that the proposed model has better prediction performance.
作者
罗丹
刘旋
LUO Dan;LIU Xuan(Xinyang Agriculture and Forestry University,Finance department,Xinyang 464000,He'nan)
出处
《电脑与电信》
2020年第8期64-67,85,共5页
Computer & Telecommunication
