Identifying the most informative features using a structurally interacting elastic net

Dual-Regularized Feature Selection for Class-Specific and Global Feature Associations

Understanding feature associations is vital for selecting the most informative features. Existing methods primarily focus on global feature associations, which capture overall relationships across all samples. However, they often overlook class-specific feature interactions, which are essential for capturing locality features that may only be significant within certain classes. In this paper, we propose Dual-Regularized Feature Selection (DRFS), which incorporates two feature association regularizers to address both class-specific and global feature relationships. The class-specific regularizer captures the local geometric structure of features within each class. Meanwhile, the global regularizer utilizes a global feature similarity matrix to eliminate redundant features across classes. By combining these regularizers, DRFS selects features that preserve both local interactions within each class and global discriminative power, with each regularizer complementing the other to enhance feature selection. Experimental results on eight public real-world datasets demonstrate that DRFS outperforms existing methods in classification accuracy.