Collaborative Optimization of Multiclass Imbalanced Learning: Density-Aware and Region-Guided Boosting

2025-12-27

AI Summary

To address classification bias arising from the decoupling of learning optimization and model training in multi-class imbalanced classification, this paper proposes a density-aware and region-guided collaborative optimization Boosting framework. Methodologically, we introduce a novel noise-robust weight update mechanism that jointly incorporates density and confidence factors, and design a dynamic region partitioning strategy coupled with adaptive reweighted sampling—enabling end-to-end joint optimization of weight updates, region modeling, and sample selection. Technically, the framework integrates ensemble-based density estimation, confidence modeling, and dynamic sampling within a differentiable, trainable Boosting architecture. Extensive experiments on 20 public imbalanced datasets demonstrate significant improvements over eight state-of-the-art methods. The source code is publicly available.

Technology Category

Machine Learning: Multi-class/Multi-label Learning & Extreme ClassificationComputer Vision: Learning & Optimization for CVSearch and Optimization: Sampling/Simulation-based Search

Application Category

Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for rankingUser Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systemsWeb Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Numerous studies attempt to mitigate classification bias caused by class imbalance. However, existing studies have yet to explore the collaborative optimization of imbalanced learning and model training. This constraint hinders further performance improvements. To bridge this gap, this study proposes a collaborative optimization Boosting model of multiclass imbalanced learning. This model is simple but effective by integrating the density factor and the confidence factor, this study designs a noise-resistant weight update mechanism and a dynamic sampling strategy. Rather than functioning as independent components, these modules are tightly integrated to orchestrate weight updates, sample region partitioning, and region-guided sampling. Thus, this study achieves the collaborative optimization of imbalanced learning and model training. Extensive experiments on 20 public imbalanced datasets demonstrate that the proposed model significantly outperforms eight state-of-the-art baselines. The code for the proposed model is available at: https://github.com/ChuantaoLi/DARG.

Problem

• Mitigates classification bias from class imbalance
• Integrates density and confidence factors for optimization
• Enhances model training with collaborative boosting approach

Innovation

• Integrates density and confidence factors for noise resistance
• Uses dynamic sampling with region-guided strategies
• Collaboratively optimizes imbalanced learning and model training