ASWBoost: Classification algorithm for noisy and imbalanced data based on parametric exponential loss

Abstract

AdaBoost, a classical boosting ensemble algorithm, is widely applied for its strong classification performance. However, its standard exponential loss is highly sensitive to outliers, prone to overfitting, and inherently biased toward the majority class under class-imbalanced settings, degrading overall performance. To address these limitations, ASWBoost, a robust boosting algorithm, is proposed by introducing a parameter θ to modify the exponential loss. New update rules for base classifier weights and training sample probability distributions are derived, enabling adaptive adjustment of sample weights. The training error upper bound of the proposed algorithm is theoretically proven, and the impact of θ on its convergence properties is rigorously analyzed. Extensive experiments on synthetic and OpenML datasets demonstrate robust performance across Balanced Accuracy, G-mean, Macro-F1, and AUC. Critically, statistical analyses confirm that ASWBoost significantly outperforms AdaBoost, CS-AdaBoost, GBDT, SMOTEBoost, and RUSBoost, with overall performance comparable to XGBoost.