Hierarchical feature selection with multi-granularity clustering structure

Abstract

Hierarchical feature selection addresses the issues caused by the presence of high-dimensional features in multi-category classification systems with hierarchical structures. Granular calculations are made to analyze the hierarchical relationships among categories when selecting the optimal feature subset. However, semantic hierarchy-based feature selection methods are prone to the semantic gap problem, which affects classification accuracy. In this paper, we propose a hierarchical feature selection method with a multi-granularity clustering structure that can effectively alleviate the semantic gap problem. Firstly, a hierarchical structure is constructed via bottom-up multi-granularity clustering based on feature similarities rather than semantic categories. This clustering hierarchy is conducive to solving semantic gap problems in the existing hierarchy. Secondly, the optimal feature subset is selected using the ℓ1,2-norms in each hierarchy’s granularity layer. This joint minimization approach can retain both the granularity layers’ shared features and granularity-specific features. Finally, we execute hierarchical classification according to the granular structure in a coarse to fine sequence. Extensive experiments demonstrate that the proposed method outperforms several state-of-the-art hierarchical feature selection approaches.