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

TL;DR

This paper introduces a collaborative optimization boosting model for multiclass imbalanced learning that integrates density and confidence factors, enhancing performance through a unified approach.

Contribution

It proposes a novel integrated boosting framework combining density-aware and region-guided strategies for imbalanced multiclass classification.

Findings

Significantly outperforms eight state-of-the-art baselines

Effective noise-resistant weight update mechanism

Dynamic sampling improves class balance handling

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.