Gradient Boosting Decision Tree

Gradient Boosting Decision Trees (GBDTs) are ensemble machine learning methods that combine multiple decision trees to achieve high predictive accuracy, particularly for tabular data. Current research focuses on improving GBDT performance in challenging scenarios, such as imbalanced datasets (through modified loss functions) and data privacy concerns (via federated learning and efficient unlearning techniques), as well as addressing inherent biases in tree construction algorithms to enhance both accuracy and interpretability. These advancements are significantly impacting various fields, including medical diagnosis, where GBDTs demonstrate superior performance compared to other methods while requiring less computational resources, and recommendation systems, where they are used for effective cross-market predictions.

Papers

September 25, 2024

Gradient Boosting Decision Trees on Medical Diagnosis over Tabular Data
A. Yarkın Yıldız, Asli Kalayci
Tabular Data Medical Diagnosis Ensemble Method Gradient Boosted Decision Tree Medical Image Classification Task Gradient Boosting Decision Tree

July 19, 2024

Improving GBDT Performance on Imbalanced Datasets: An Empirical Study of Class-Balanced Loss Functions
Jiaqi Luo, Yuan Yuan, Shixin Xu
Empirical Study Class Imbalance Imbalanced Datasets Class Loss Balanced Loss Gradient Boosting Decision Tree Tabular Classification

November 22, 2023

SecureCut: Federated Gradient Boosting Decision Trees with Efficient Machine Unlearning
Jian Zhang, Bowen Li Jie Li, Chentao Wu
Machine Unlearning Unlearning Framework Vertical Federated Learning Cut and Approximate Gradient Boosting Decision Tree Data Deletion Federated Gradient Boosting

May 18, 2023

Unbiased Gradient Boosting Decision Tree with Unbiased Feature Importance
Zheyu Zhang, Tianping Zhang, Jian Li
Permutation Importance Gradient Boosted Decision Tree Unbiased SGG Gradient Boosting Decision Tree

October 20, 2022

Improving Data Quality with Training Dynamics of Gradient Boosting Decision Trees
Moacir Antonelli Ponti, Lucas de Angelis Oliveira, Mathias Esteban, Valentina Garcia, Juan Martín Román, Luis Argerich
Decision Tree Data Quality Training Dynamic Real World Datasets Gradient Boosting Decision Tree

July 20, 2022

Quantized Training of Gradient Boosting Decision Trees
Yu Shi, Guolin Ke, Zhuoming Chen, Shuxin Zheng, Tie-Yan Liu
Low Precision Training Gradient Boosting Decision Tree

June 19, 2022

Generational Differences in Automobility: Comparing America's Millennials and Gen Xers Using Gradient Boosting Decision Trees
Kailai Wang, Xize Wang
Distance Information Different Age Gradient Boosting Decision Tree

February 17, 2022

Multi-stage Ensemble Model for Cross-market Recommendation
Cesare Bernardis
State of the Art Ranking Consistency Team AcieLee Gradient Boosting Decision Tree Cross Market Recommendation