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Improving GBDT Performance on Imbalanced Datasets: An Empirical Study of
  Class-Balanced Loss Functions

Improving GBDT Performance on Imbalanced Datasets: An Empirical Study of Class-Balanced Loss Functions

19 July 2024
Jiaqi Luo
Yuan Yuan
Shixin Xu
    AI4CE
ArXivPDFHTML

Papers citing "Improving GBDT Performance on Imbalanced Datasets: An Empirical Study of Class-Balanced Loss Functions"

3 / 3 papers shown
Title
Influence-Balanced Loss for Imbalanced Visual Classification
Influence-Balanced Loss for Imbalanced Visual Classification
Seulki Park
Jongin Lim
Younghan Jeon
J. Choi
CVBM
79
129
0
06 Oct 2021
Equalization Loss for Long-Tailed Object Recognition
Equalization Loss for Long-Tailed Object Recognition
Jingru Tan
Changbao Wang
Buyu Li
Quanquan Li
Wanli Ouyang
Changqing Yin
Junjie Yan
237
455
0
11 Mar 2020
SMOTE: Synthetic Minority Over-sampling Technique
SMOTE: Synthetic Minority Over-sampling Technique
Nitesh V. Chawla
Kevin W. Bowyer
Lawrence Hall
W. Kegelmeyer
AI4TS
160
25,150
0
09 Jun 2011
1