GPMFS: Global Foundation and Personalized Optimization for Multi-Label Feature Selection
Journal:
arXiv
Published Date:
Apr 17, 2025
Abstract
As artificial intelligence methods are increasingly applied to complex task
scenarios, high dimensional multi-label learning has emerged as a prominent
research focus. At present, the curse of dimensionality remains one of the
major bottlenecks in high-dimensional multi-label learning, which can be
effectively addressed through multi-label feature selection methods. However,
existing multi-label feature selection methods mostly focus on identifying
global features shared across all labels, which overlooks personalized
characteristics and specific requirements of individual labels. This
global-only perspective may limit the ability to capture label-specific
discriminative information, thereby affecting overall performance. In this
paper, we propose a novel method called GPMFS (Global Foundation and
Personalized Optimization for Multi-Label Feature Selection). GPMFS firstly
identifies global features by exploiting label correlations, then adaptively
supplements each label with a personalized subset of discriminative features
using a threshold-controlled strategy. Experiments on multiple real-world
datasets demonstrate that GPMFS achieves superior performance while maintaining
strong interpretability and robustness. Furthermore, GPMFS provides insights
into the label-specific strength across different multi-label datasets, thereby
demonstrating the necessity and potential applicability of personalized feature
selection approaches.
