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自监督 LightGBM

自监督 LightGBM 将自监督学习范式与 LightGBM 梯度提升框架相结合，以利用大量的无标签表格数据。通过掩码特征预测或对比性损坏等自监督预设任务，可以生成丰富的特征表示或伪标签，然后用于训练或微调 LightGBM 模型，从而在标签稀疏的场景下显著提高性能。

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自监督 LightGBM
梯度提升（Gradient Boosting）LightGBM自监督学习半监督 LightGBM迁移学习XGBoost

来源

  1. Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T.-Y. (2017). LightGBM: A Highly Efficient Gradient Boosting Decision Tree. Advances in Neural Information Processing Systems, 30. link ↗
  2. Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020). A Simple Framework for Contrastive Self-Supervised Learning. Proceedings of the 37th International Conference on Machine Learning (ICML). link ↗

如何引用本页

ScholarGate. (2026, June 3). Self-supervised Learning with LightGBM (Gradient Boosting with Self-supervised Pretraining). ScholarGate. https://scholargate.app/zh/machine-learning/self-supervised-lightgbm

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ScholarGate — Self-supervised LightGBM (Self-supervised Learning with LightGBM (Gradient Boosting with Self-supervised Pretraining)). 于 2026-06-15 检索自 https://scholargate.app/zh/machine-learning/self-supervised-lightgbm · 数据集： https://doi.org/10.5281/zenodo.20539026