Fast Genetic Algorithm for Feature Selection: A Qualitative Approximation Approach

The authors propose a two-stage surrogate-assisted evolutionary approach to address the computational issues arising from using Genetic Algorithm (GA) for feature selection in a wrapper setting for large datasets. They define "Approximation Usefulness" to capture the necessary conditions to ensure correctness of the EA computations when an approximation is used, and propose a procedure to construct a lightweight qualitative meta-model by the active selection of data instances. This meta-model is then used to carry out the feature selection task efficiently.