Adaptive stochastic gradient boosting tree with composite criterion

作者：Li, Lin; Li, Yang^*; Qin, Yichen; Chen, Jiaxu; Wang, Limin; Yi, Danhui

来源：Journal of Statistical Computation and Simulation, 2016, 86(10): 1901-1911.

DOI：10.1080/00949655.2015.1090988

• Stochastic gradient boosting tree
• suboptimal health-state study
• cost-sensitivity

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.

• 出版日期2016-7-2
• 单位北京中医药大学; 中国人民大学