基于MCP惩罚的群组变量选择和AdaBoost集成剪枝  

MCP-Based Method in Group Variable Selection and AdaBoost Ensemble-Pruning

作　　者：万红燕[1] 张云云 WAN Hong-Yan;ZHANG Yun-Yun(School of Management,University of Science and Technology of China,Hefei 230026,China)

机构地区：[1]中国科学技术大学管理学院,合肥230026

出　　处：《计算机系统应用》2021年第11期281-288,共8页Computer Systems & Applications

摘　　要：针对高维群组变量下的分类问题,本文提出了一种基于MCP惩罚的AdaBoost集成剪枝逻辑回归模型(AdaMCPLR),将MCP函数同时应用于特征选择和集成剪枝,在简化模型的同时有效地提升了预测精度.由于传统的坐标下降算法效率较低,本文引用并改进了PICASSO算法使其能够应用于群组变量选择,大大提高了模型的求解效率.通过模拟实验,发现AdaMCPLR方法的变量选择和分类预测效果均优于其他预测方法.最后,本文将提出的AdaMCPLR方法应用于我国上市公司财务困境预测中.To tackle the classification problem of high-dimensional group variables, this study proposes an MCP-based AdaBoost ensemble-pruning logistic regression model(AdaMCPLR). The MCP function is applied to feature selection and ensemble pruning simultaneously, which not only simplifies the model, but also effectively improves the prediction accuracy. For the efficiency enhancement, this paper improves the PICASSO algorithm to make it applicable to group variable selection. Simulation experiments show that the AdaMCPLR method is superior to other prediction methods in variable selection and classification prediction. Finally, the AdaMCPLR method proposed in this study is applied to the financial distress prediction of listed companies in China.

关 键 词：ADABOOST 集成剪枝 双层变量选择 财务困境预测 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论] O213[自动化与计算机技术—计算机科学与技术]