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Imbalance-XGBoost: Leveraging Weighted and Focal Losses for Binary Label-Imbalanced Classification with XGBoost

Imbalance-XGBoost: Leveraging Weighted and Focal Losses for Binary Label-Imbalanced Classification with XGBoost

Chen Wang
Chengyuan Deng
Suzhen Wang
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Papers citing "Imbalance-XGBoost: Leveraging Weighted and Focal Losses for Binary Label-Imbalanced Classification with XGBoost"

3 / 3 papers shown
Title
Tree Boosting Methods for Balanced andImbalanced Classification and their Robustness Over Time in Risk Assessment
Tree Boosting Methods for Balanced andImbalanced Classification and their Robustness Over Time in Risk Assessment
Gissel Velarde
Michael Weichert
Anuj Deshmunkh
Sanjay Deshmane
Anindya Sudhir
K. Sharma
Vaibhav Joshi
70
3
0
25 Apr 2025
Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation
Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation
D. Powers
16
5,240
0
11 Oct 2020
Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning
Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning
G. Lemaître
Fernando Nogueira
Christos K. Aridas
23
2,052
0
21 Sep 2016