Regularized receiver operating characteristic-based logistic regression for grouped variable selection with composite criterion

Yang Li, Chenqun Yu, Yichen Qin, Limin Wang, Jiaxu Chen, Danhui Yi, Ben-Chang Shia, Shuangge Ma

研究成果: 雜誌貢獻文章

3 引文 斯高帕斯(Scopus)

摘要

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.

原文英語
頁(從 - 到)2582-2595
頁數14
期刊Journal of Statistical Computation and Simulation
85
發行號13
DOIs
出版狀態已發佈 - 九月 2 2015
對外發佈Yes

ASJC Scopus subject areas

  • Applied Mathematics
  • Statistics and Probability
  • Modelling and Simulation
  • Statistics, Probability and Uncertainty

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