GAdaBoost: An Efficient and Robust AdaBoost Algorithm Based on Granular-Ball Structure
This work addresses label noise issues in AdaBoost for multiclass classification, offering an incremental improvement over existing methods.
The paper tackles AdaBoost's vulnerability to label noise in multiclass classification by proposing GAdaBoost, a two-stage framework using granular-ball structures to compress data and reduce noise sensitivity, achieving superior robustness and efficiency on noisy datasets.
Adaptive Boosting (AdaBoost) faces significant challenges posed by label noise, especially in multiclass classification tasks. Existing methods either lack mechanisms to handle label noise effectively or suffer from high computational costs due to redundant data usage. Inspired by granular computing, this paper proposes granular adaptive boosting (GAdaBoost), a novel two-stage framework comprising a data granulation stage and an adaptive boosting stage, to enhance efficiency and robustness under noisy conditions. To validate its feasibility, an extension of SAMME, termed GAdaBoost.SA, is proposed. Specifically, first, a granular-ball generation method is designed to compress data while preserving diversity and mitigating label noise. Second, the granular ball-based SAMME algorithm focuses on granular balls rather than individual samples, improving efficiency and reducing sensitivity to noise. Experimental results on some noisy datasets show that the proposed approach achieves superior robustness and efficiency compared with existing methods, demonstrating that this work effectively extends AdaBoost and SAMME.