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The paper examines XGBoost's sensitivity to outliers in regression tasks and introduces robust loss functions based on M-, S-, and τ-estimators, proposing MM-XGBoost for a better trade-off between robustness and predictive accuracy.
This paper proposes the Intuitionistic Fuzzy Graph Embedded Random Vector Functional Link with Multiview Learning (IFGRVFL-MV) model, which integrates intuitionistic fuzzy sets, graph embedding, and multiview learning to improve classification accuracy and robustness to outliers. Experiments on benchmark datasets show that IFGRVFL-MV outperforms existing models.