Conditional characteristic feature screening for massive imbalanced data

Using conditional characteristic function as a screening index, a new model-free screening procedure is proposed to deal with variable screening problems in large-scale high-dimensional imbalanced data analysis. For binary response, our results show that the screening index under full data is proportional to the screening index under case–control sampling, an important sampling property for imbalanced data. This conclusion implies that we can apply this screening method to imbalanced data. Surely, the most appealing feature of the screening index is that it can be expressed as a simple linear combination of two first-order moments, so it is computationally simple. In addition, we successfully extend this method to multiple response. The theoretical properties are established under regularity conditions. To compare the performance of our method with its competitors, extensive simulations are conducted, which shows that the proposed procedure performs well in both the linear and nonlinear models. Finally, a real data analysis is investigated to further illustrate the effectiveness of the new method.