Adaptive stochastic gradient boosting tree with composite criterion. Issue 10 (2nd July 2016)

• Record Type: Journal Article
• Authors: Li, Lin; Li, Yang; Qin, Yichen; Chen, Jiaxu; Wang, Limin; Yi, Danhui
• Is Part Of: Journal of statistical computation and simulation. Volume 86:Issue 10(2016)
• Page Start: 1901
• Page End: 1911
• Publication Date: 2016-07-02
• Subjects: Stochastic gradient boosting tree -- cost-sensitivity -- suboptimal health-state study; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5028505
• DOI: 10.1080/00949655.2015.1090988
• Languages: English
• ISSNs: 0094-9655

Abstract

ABSTRACT: In this paper, we propose an adaptive stochastic gradient boosting tree for classification studies with imbalanced data. The adjustment of cost-sensitivity and the predictive threshold are integrated together with a composite criterion into the original stochastic gradient boosting tree to deal with the issues of the imbalanced data structure. Numerical study shows that the proposed method can significantly enhance the classification accuracy for the minority class with only a small loss in the true negative rate for the majority class. We discuss the relation of the cost-sensitivity to the threshold manipulation using simulations. An illustrative example of the analysis of suboptimal health-state data in traditional Chinese medicine is discussed.