Influence-Balanced XGBoost: Improving XGBoost for Imbalanced Data Using Influence Functions
Decision tree boosting algorithms, such as XGBoost, have demonstrated superior predictive performance on tabular data for supervised learning compared to neural networks. However, recent studies on loss functions for imbalanced data have primarily focused on deep learning. The goal of this study is...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10807295/ |
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