A novel sensitivity-based method for feature selection

Abstract

Sensitivity analysis is a popular feature selection approach employed to identify the important features in a dataset. In sensitivity analysis, each input feature is perturbed one-at-a-time and the response of the machine learning model is examined to determine the feature's rank. Note that the existing perturbation techniques may lead to inaccurate feature ranking due to their sensitivity to perturbation parameters. This study proposes a novel approach that involves the perturbation of input features using a complex-step. The implementation of complex-step perturbation in the framework of deep neural networks as a feature selection method is provided in this paper, and its efficacy in determining important features for real-world datasets is demonstrated. Furthermore, the filter-based feature selection methods are employed, and the results obtained from the proposed method are compared. While the results obtained for the classification task indicated that the proposed method outperformed other feature ranking methods, in the case of the regression task, it was found to perform more or less similar to that of other feature ranking methods.