|
|||||
|
|
Grouped Variable Selection Using Area under the ROC with Imbalanced Data |
|
| Authors: | Yang Li Yichen Qin Limin Wang Jiaxu Chen Shuangge Ma |
| Institution: | 1. Center for Applied Statistics, Renmin University of China, Beijing, P.R. China;2. School of Statistics, Renmin University of China, Beijing, P.R. China;3. Department of Operations, Business Analytics and Information Systems, University of Cincinnati, Ohio, USA;4. School of Preclinical Medicine, Beijing University of Chinese Medicine, Beijing, P.R. China;5. School of Statistics, Renmin University of China, Beijing, P.R. China;6. Department of Biostatistics, Yale University, New Haven, Connecticut, USA |
| Abstract: | Imbalanced data brings biased classification and causes the low accuracy of the classification of the minority class. In this article, we propose a methodology to select grouped variables using the area under the ROC with an adjustable prediction cut point. The proposed method enhance the accuracy of classification for the minority class by maximizing the true positive rate. Simulation results show that the proposed method is appropriate for both the categorical and continuous covariates. An illustrative example of the analysis of the SHS data in TCM is discussed to show the reasonable application of the proposed method. |
| Keywords: | Area under ROC Group lasso Imbalanced data True positive rate |