A global and local unified feature selection algorithm based on hierarchical structure constraints

Abstract

Existing feature selection methods face challenges when applied to hierarchically structured data, which can be primarily due to a lack of synergy between nodes, resulting in impaired global consistency and poor local coherence. For example, parent nodes may dominate feature weighting without child feedback (e.g., suppressing texture details in fine-grained image classification), while sibling nodes fail to capture asymmetric dependencies in shared features (e.g., genetic markers varying across disease subtypes). To address these issues, a Global and Local Unified Feature Selection algorithm was proposed based on Hierarchical Structure Constraints (GLUFS-HSC). This algorithm integrated global and local perspectives and introduced a bidirectional consistency constraint mechanism for parent–child nodes, along with an asymmetry constraint mechanism for sibling nodes. These innovations enhanced feature selection efficiency and inter-level coordination. The algorithm employed a multi-objective optimization framework to maintain consistency while preserving the original data features. At the global level, it incorporated node relationships and hierarchical requirements by iteratively updating a weight matrix. At the local level, the traditional one-way dependency or implicit bidirectional models were replaced with an explicit parent–child bidirectional consistency constraint, enabling the parent nodes to dynamically adjust the weight distribution based on feedback from child nodes. This approach facilitated information transfer and strengthened hierarchical synergy. For sibling nodes, an asymmetric constraint mechanism combining HSIC constraint and orthogonal constraint is introduced to effectively capture feature differences, reduce feature redundancy, and enhance feature independence and correlation. Experimental comparisons across eight datasets demonstrated that GLUFS-HSC achieved superior performance on hierarchically structured data, significantly improving the consistency and accuracy of feature selection.