GPMFS: Global Foundation and Personalized Optimization for Multi-Label Feature Selection

• URL: http://arxiv.org/abs/2504.12740v1
• Date: Thu, 17 Apr 2025 08:29:14 GMT
• Title: GPMFS: Global Foundation and Personalized Optimization for Multi-Label Feature Selection
• Authors: Yifan Cao, Zhilong Mi, Ziqiao Yin, Binghui Guo, Jin Dong,
• Abstract summary: The curse of dimensionality remains one of the major bottlenecks in high-dimensional multi-label learning.<n>We propose a novel method called GPMFS (Global Foundation and Personalized Optimization for Multi-Label Feature Selection)<n>Experiments on multiple real-world datasets demonstrate that GPMFS achieves superior performance while maintaining strong interpretability and robustness.
• Score: 0.820217860574125
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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