Regularized receiver operating characteristic-based logistic regression for grouped variable selection with composite criterion |
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Authors: | Yang Li Chenqun Yu Yichen Qin Limin Wang Jiaxu Chen Danhui Yi |
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Affiliation: | 1. Center for Applied Statistics, Renmin University of China, Beijing, People's Republic of China;2. School of Statistics, Renmin University of China, Beijing, People's Republic of China;3. Statistical Consulting Center, Renmin University of China, Beijing, People's Republic of China;4. School of Statistics, Renmin University of China, Beijing, People's Republic of China;5. Department of Operations, Business Analytics and Information Systems, University of Cincinnati, Cincinnati, OH, USA;6. School of Pre-Clinical Medicine, Beijing University of Chinese Medicine, Beijing, People's Republic of China |
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Abstract: | It is well known that statistical classifiers trained from imbalanced data lead to low true positive rates and select inconsistent significant variables. In this article, an improved method is proposed to enhance the classification accuracy for the minority class by differentiating misclassification cost for each group. The overall error rate is replaced by an alternative composite criterion. Furthermore, we propose an approach to estimate the tuning parameter, the composite criterion, and the cut-point simultaneously. Simulations show that the proposed method achieves a high true positive rate on prediction and a good performance on variable selection for both continuous and categorical predictors, even with highly imbalanced data. An illustrative example of the analysis of the suboptimal health state data in traditional Chinese medicine is discussed to show the reasonable application of the proposed method. |
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Keywords: | imbalanced data group lasso composite criterion true positive rate |
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