An improved GEV boosting method for imbalanced data classification with application to short-term rainfall prediction.

This paper considers the imbalanced binary classification problem by focusing on the application of the short-term rainfall forecasting in arid and semi-arid regions. Specifically, we present a novel boosting-type method by utilizing the generalized extreme value (GEV) distribution as the link function and applying a gradient tree boosting algorithm to capture complex interactions among covariates. The proposed method has several appealing advantages such as, it can identify rare rainfall events as well as quantifying the uncertainties; it is data-driven that without any assumption on the relationship between the covariates and the rainfall event; the fitted model is highly interpretable, making it a useful tool for studying the rainfall mechanisms in arid and semi-arid regions. Experiments on two real-world datasets show that our approach outperforms its competing methods. • Propose the generalized extreme value distribution as the skewed link function. • Employ the gradient tree boosting to capture complex interactions among covariates. • Can identify the rare rainfall events as well as quantifying the uncertainties. • No assumption on the relationship between the covariates and the rainfall event. • The model for fitting the imbalanced data is highly interpretable. [ABSTRACT FROM AUTHOR]