A Review of Ensemble Learning Based Feature Selection

Feature selection is an important topic in machine learning. In recent years, via integrating ensemble learning, the ensemble learning based feature selection approach has been proposed and studied. The general idea is to generate multiple diverse feature selectors and combine their outputs. This approach is superior to conventional feature selection methods in many aspects. Among them, its most prominent advantage is the ability to handle stability issue that is usually poor in existing feature selection methods. This review covers different issues related to ensemble learning based feature selection, which include the main modules, the stability measurement, etc. To the best of our knowledge, this is the first review that focuses on ensemble feature selection. It can be a useful reference in the literature of feature selection.