Random Subspace Evolutionary Feature Selection for High-dimensional Data

Classification represents a fundamental task in machine learning and data mining. Many real-world datasets exhibit a large number of features but only a limited number of instances, which often poses challenges for conventional classification algorithms. Dealing with such datasets, one prevalent approach involves using feature selection to retain relevant features while eliminating irrelevant ones. Although evolutionary computation algorithms have been extensively used for such problems, they do not perform well with these high-dimensional datasets. To address these limitations, this paper introduces a random subspace evolutionary feature selection method that performs feature selection on small subsets of features to avoid overfitting and speed up the process and employs multiple feature subspaces to prevent stagnation in local optima during feature selection. The experimental results demonstrate that this novel method achieves higher accuracy and greater stability compared to other commonly used benchmark feature selection techniques.