A Scalable Curiosity-Driven Game-Theoretic Framework for Long-Tail Multi-Label Learning in Data Mining

Technical Deep Dive: A Scalable Curiosity-Driven Game-Theoretic Framework for Long-Tail Multi-Label Learning in Data Mining

Overview

The proposed Curiosity-Driven Game-Theoretic Multi-Label Learning (CD-GTMLL) framework reframes the long-tail multi-label classification (MLC) problem as a cooperative multi-player game. Multiple specialized "players" (sub-predictors) collaborate to maximize overall accuracy while receiving a curiosity reward that scales with label rarity and inter-player disagreement. This intrinsic exploration mechanism dynamically steers learning toward tail labels without manual tuning.

Problem & Context

• Multi-label classification in real-world datasets exhibits a long-tail distribution, where a few "head" labels dominate while most "tail" labels are rarely observed.
• Existing methods like resampling, reweighting, or ensemble techniques have limitations in addressing this challenge at scale, especially as the label space grows to tens of thousands of labels.

Methodology

Long-Tail MLC Formulation

• The label set L is partitioned into head labels LH and tail labels LT based on label frequency.
• The framework decomposes the label space into N overlapping subsets assigned to cooperating "players" (sub-predictors).
• Players collaborate to maximize a shared global payoff (overall classification accuracy).

Game-Theoretic Formulation

• Each player i maximizes Ji(θi) = R(θ) + αCi(x; θ), where R is the global accuracy and Ci is a curiosity reward.
• The curiosity reward amplifies correct predictions on infrequent labels and leverages inter-player disagreement as an exploration signal.
• Theorem 1 proves the existence of a tail-aware Nash equilibrium, where no equilibrium can systematically ignore tail labels.

Learning Algorithm

• Players are updated sequentially via a cyclic best-response procedure, with each player optimizing its local objective.
• The computational complexity scales linearly with the label count L.

Data & Experimental Setup

• Evaluated on standard MLC benchmarks (Pascal VOC, MS-COCO, Yeast, Mediamill) and extreme multi-label datasets (Eurlex-4K, Wiki10-31K, AmazonCat-13K).
• Compared to representative baselines covering resampling, reweighting, ensemble, and other state-of-the-art methods.
• Metrics include mAP, Micro-F1, Macro-F1, and the Rare-F1 score focused on tail labels.

Results

• CD-GTMLL consistently outperforms baselines on both head-centric and tail-centric metrics across all standard datasets.
• On Pascal VOC, it achieves 92.8% mAP and 79.4% Rare-F1, surpassing the next best by 0.9 and 1.2 percentage points, respectively.
• Under artificially intensified long-tail conditions, CD-GTMLL's advantage in Rare-F1 becomes more pronounced, e.g., 64.3% on Pascal VOC-R@50% vs. 62.9% for the runner-up.

Interpretation

• Ablation studies confirm the critical contributions of both the curiosity mechanism and the multi-player game structure.
• Analysis of the players' behavior reveals emergent specialization, with individual players becoming experts on head or tail labels.
• The training dynamics show that the rapid decrease in tail-label disagreement corresponds to the fastest gain in the overall system potential, validating the framework's efficiency.
• The computational overhead scales linearly with the number of players, representing a worthwhile trade-off for the substantial performance boost, especially on extreme-scale datasets.

Limitations & Uncertainties

• The theoretical analysis assumes certain conditions, such as the continuity and compactness of the parameter space and the tail-responsiveness of the performance metric.
• While the framework demonstrates strong empirical performance, its behavior and guarantees may differ on datasets or tasks that violate these assumptions.
• The practical implications of the framework's hyperparameters, such as the number of players and the curiosity weight, require further investigation across a broader range of applications.

What Comes Next

• Extending the framework to handle dynamic, evolving label spaces, where the tail distribution may shift over time.
• Exploring potential synergies between the game-theoretic exploration and other representation learning techniques, such as contrastive pre-training or meta-transfer learning.
• Investigating the applicability of the cooperative game formulation to other long-tail learning problems beyond multi-label classification.