Abstract: With the emergence of massive high-dimensional data in many research and application fields, it is crucial to mine valuable information by using the sparsity of high-dimensional data. Being an effective tool for building an interpretative model, improving inference and prediction accuracy, variable selection plays an increasingly important role in statistical modelling of high-dimensional data. Because ensemble learning has advantages to significantly improve selection accuracy, to alleviate the instability of traditional selection methods, and to reduce falsely including noise variables, variable selection ensemble (VSE) methods have gained considerable interest in context of variable selection. In order to provide a systematic reference for researchers in related fields, this paper presents a detailed survey of the existing VSEs and categorizes them into two classes according to their different strategies. The main characteristics of the methods in each class are also analyzed. In the meantime, some simulated experiments are carried out to investigate the selection and prediction performance of some representative VSE techniques. Finally, several research directions of VSEs deserved to be further studied are discussed.

Key words: high-dimensional data analysis, variable selection, linear regression model, ensemble learning, stability