Computational Cost Reduction in Wrapper Approaches for Feature Selection: A Case of Study Using Permutational-Based Differential Evolution

Wrapper approaches for feature selection are known for their high performance, but the drawback of high computational cost is presented. This work proposes using cost-reduction mechanisms applied to the permutational-based Differential Evolution (DE-FS
^PM
) algorithm for feature selection. Two proposals considering fixed and incremental sampling fraction strategies are considered to reduce the cost of evaluating an individual. A memory mechanism for avoiding repeated evaluations is included. The success-history parameter adaptation for Differential Evolution (SHADE) procedure adapted to the permutational search space is applied in two additional proposals. Eighteen datasets were used for experimentation. The fixed sampling fraction proposal with the memory mechanism reached competitive accuracy results while requiring less computational time. The sampling strategies could effectively reduce the number of dataset instances used for evaluation. In addition, the memory mechanism avoids a fraction of the evaluations in the search process. The results show that two simple mechanisms can effectively decrease the computational cost of a wrapper approach for feature selection without diminishing its performance.