A Novel Genetic Algorithm for Feature Selection in Hierarchical Feature Spaces

Feature selection methods have been widely adopted to prepare high-dimensional feature spaces for the classification task of data mining. However, in many real-world datasets, the feature space is formed by binary features related via generalization-specialization relationships, also known as hierarchical feature spaces. Although there are many methods for the traditional feature selection problem, methods which properly consider hierarchical features are still very underexplored. In this work, we propose a novel genetic algorithm (GA) for hierarchical feature selection. The proposed GA has two novel hierarchical mutation operators tailored to deal with redundant features in hierarchical feature spaces. The computational experiments show that our proposed approach exhibited better predictive performance than two state-of-the-art hierarchical feature selection methods (SHSEL and HIP) and also than two traditional feature selection methods (ReliefF and CFS).