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

2025-04-17

AI Summary

Existing multi-label feature selection methods overly rely on globally shared features while neglecting label-specific discriminative requirements, thereby limiting classification performance. To address this, we propose Global-Personalized Multi-label Feature Selection (GPMFS), a novel framework that decouples and synergistically integrates global modeling with label-level optimization. GPMFS first identifies a shared base feature set by leveraging label correlations; subsequently, it adaptively selects discriminative, label-specific feature subsets for each label. The method jointly incorporates label correlation modeling, a threshold-controllable personalized feature supplementation mechanism, and an interpretable feature importance evaluation scheme. Extensive experiments on multiple real-world benchmark datasets demonstrate significant improvements in multi-label classification accuracy, while maintaining strong interpretability and cross-dataset robustness.

Technology Category

• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Computer Vision: Multi-modal Vision
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Addresses high-dimensional multi-label learning challenges
• Overcomes limitations of global-only feature selection
• Balances global and personalized label-specific features

Innovation

• Combines global and personalized feature selection
• Uses threshold-controlled strategy for adaptation
• Exploits label correlations for global features