Robust-GBDT: GBDT with Nonconvex Loss for Tabular Classification in the Presence of Label Noise and Class Imbalance

• URL: http://arxiv.org/abs/2310.05067v2
• Date: Sat, 16 Mar 2024 01:17:11 GMT
• Title: Robust-GBDT: GBDT with Nonconvex Loss for Tabular Classification in the Presence of Label Noise and Class Imbalance
• Authors: Jiaqi Luo, Yuedong Quan, Shixin Xu,
• Abstract summary: Robust-GBDT is a groundbreaking approach that combines the resilience of non loss functions against label noise. It significantly enhances capabilities, particularly in noisy and imbalanced datasets. It paves the way for a new era of robust and precise classification across diverse real-world applications.
• Score: 0.40964539027092917
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Dealing with label noise in tabular classification tasks poses a persistent challenge in machine learning. While robust boosting methods have shown promise in binary classification, their effectiveness in complex, multi-class scenarios is often limited. Additionally, issues like imbalanced datasets, missing values, and computational inefficiencies further complicate their practical utility. This study introduces Robust-GBDT, a groundbreaking approach that combines the power of Gradient Boosted Decision Trees (GBDT) with the resilience of nonconvex loss functions against label noise. By leveraging local convexity within specific regions, Robust-GBDT demonstrates unprecedented robustness, challenging conventional wisdom. Through seamless integration of advanced GBDT with a novel Robust Focal Loss tailored for class imbalance, Robust-GBDT significantly enhances generalization capabilities, particularly in noisy and imbalanced datasets. Notably, its user-friendly design facilitates integration with existing open-source code, enhancing computational efficiency and scalability. Extensive experiments validate Robust-GBDT's superiority over other noise-robust methods, establishing a new standard for accurate classification amidst label noise. This research heralds a paradigm shift in machine learning, paving the way for a new era of robust and precise classification across diverse real-world applications.

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