High-dimensional feature selection via feature grouping: A Variable Neighborhood Search approach
We introduce the concept of predominant group based on the idea of Markov blanket to identify groups of correlated features. We propose a greedy strategy (GreedyPGG) that groups features based on the concept of predominant groups. We propose a VNS metaheuristic that uses the GreedyPGG strategy to reduce the dimensionality in high-dimensional data. Results show that VNS finds smaller subsets of features without degrading the predictive model.
feature selection feature grouping metaheuristic high dimensionality