A Scalable Curiosity-Driven Game-Theoretic Framework for Long-Tail Multi-Label Learning in Data Mining
arXiv:2602.15330v1 Announce Type: new Abstract: The long-tail distribution, where a few head labels dominate while rare tail labels abound, poses a persistent challenge for large-scale Multi-Label Classification (MLC) in real-world data mining applications. Existing resampling and reweighting strategies often disrupt inter-label dependencies or require brittle hyperparameter tuning, especially as the label space expands to tens of thousands of labels. To address this issue, we propose Curiosity-Driven Game-Theoretic Multi-Label Learning (CD-GTMLL), a scalable cooperative framework that recasts long-tail MLC as a multi-player game - each sub-predictor ("player") specializes in a partition of the label space, collaborating to maximize global accuracy while pursuing intrinsic curiosity rewards based on tail label rarity and inter-player disagreement. This mechanism adaptively injects learning signals into under-represented tail labels without manual balancing or tuning. We further provid
arXiv:2602.15330v1 Announce Type: new Abstract: The long-tail distribution, where a few head labels dominate while rare tail labels abound, poses a persistent challenge for large-scale Multi-Label Classification (MLC) in real-world data mining applications. Existing resampling and reweighting strategies often disrupt inter-label dependencies or require brittle hyperparameter tuning, especially as the label space expands to tens of thousands of labels. To address this issue, we propose Curiosity-Driven Game-Theoretic Multi-Label Learning (CD-GTMLL), a scalable cooperative framework that recasts long-tail MLC as a multi-player game - each sub-predictor ("player") specializes in a partition of the label space, collaborating to maximize global accuracy while pursuing intrinsic curiosity rewards based on tail label rarity and inter-player disagreement. This mechanism adaptively injects learning signals into under-represented tail labels without manual balancing or tuning. We further provide a theoretical analysis showing that our CD-GTMLL converges to a tail-aware equilibrium and formally links the optimization dynamics to improvements in the Rare-F1 metric. Extensive experiments across 7 benchmarks, including extreme multi-label classification datasets with 30,000+ labels, demonstrate that CD-GTMLL consistently surpasses state-of-the-art methods, with gains up to +1.6% P@3 on Wiki10-31K. Ablation studies further confirm the contributions of both game-theoretic cooperation and curiosity-driven exploration to robust tail performance. By integrating game theory with curiosity mechanisms, CD-GTMLL not only enhances model efficiency in resource-constrained environments but also paves the way for more adaptive learning in imbalanced data scenarios across industries like e-commerce and healthcare.
Executive Summary
This article proposes a novel framework, Curiosity-Driven Game-Theoretic Multi-Label Learning (CD-GTMLL), to address the long-tail distribution challenge in large-scale Multi-Label Classification (MLC) tasks. CD-GTMLL recasts long-tail MLC as a multi-player game, where sub-predictors specialize in label space partitions and collaborate to maximize global accuracy. The framework injects learning signals into under-represented tail labels using intrinsic curiosity rewards. Experiments demonstrate CD-GTMLL's superiority over state-of-the-art methods on 7 benchmarks, including extreme multi-label classification datasets with 30,000+ labels. This framework has the potential to enhance model efficiency in resource-constrained environments and pave the way for adaptive learning in imbalanced data scenarios.
Key Points
- ▸ CD-GTMLL recasts long-tail MLC as a multi-player game for scalable and cooperative learning.
- ▸ Intrinsic curiosity rewards facilitate learning signals for under-represented tail labels.
- ▸ CD-GTMLL outperforms state-of-the-art methods on 7 benchmarks with up to +1.6% P@3 gain.
- ▸ The framework adapts to resource-constrained environments and imbalanced data scenarios.
Merits
Strength in Scalability
CD-GTMLL's game-theoretic framework enables scalable learning for large-scale MLC tasks with tens of thousands of labels.
Efficient Learning in Resource-Constrained Environments
The framework's adaptive learning signals facilitate efficient model training in resource-constrained environments.
Improved Tail Performance
CD-GTMLL's intrinsic curiosity rewards improve Rare-F1 metric performance in under-represented tail labels.
Demerits
Limited Theoretical Analysis
While the article provides some theoretical analysis, more in-depth analysis of convergence and equilibrium properties would strengthen the framework's foundation.
High Computational Complexity
CD-GTMLL's multi-player game framework may require significant computational resources, potentially limiting its practical application in real-world scenarios.
Expert Commentary
While CD-GTMLL demonstrates promising results, further investigation into its computational complexity and theoretical analysis is necessary to fully validate its potential. Additionally, exploring applications beyond MLC tasks and integrating CD-GTMLL with other machine learning frameworks could further enhance its impact.
Recommendations
- ✓ Future research should focus on optimizing CD-GTMLL's computational complexity and exploring its applications in other machine learning domains.
- ✓ Developing a more comprehensive theoretical analysis of CD-GTMLL's convergence and equilibrium properties will strengthen its foundation and facilitate wider adoption.
