GAdaBoost: An Efficient and Robust AdaBoost Algorithm Based on Granular-Ball Structure

TL;DR

GAdaBoost introduces a two-stage granular computing framework to improve AdaBoost's robustness and efficiency in noisy multiclass classification, effectively reducing sensitivity to label noise and computational costs.

Contribution

It proposes a novel granular-ball based boosting method that enhances robustness and efficiency under noisy conditions, extending AdaBoost and SAMME with a data compression approach.

Findings

Outperforms existing methods in noisy datasets

Reduces computational costs significantly

Improves robustness against label noise

Abstract

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.