首页GBDT算法近五年参考文献

GBDT算法近五年参考文献

时间: 2023-10-22 22:33:31 浏览: 199

以下是近五年GBDT算法的参考文献： 1. Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of statistics, 29(5), 1189-1232. 2. Chen, T., & Guestrin, C. (2016). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining (pp. 785-794). 3. Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., ... & Liu, T. Y. (2017). Lightgbm: A highly efficient gradient boosting decision tree. In Advances in neural information processing systems (pp. 3146-3154). 4. Chen, T., He, T., Benesty, M., Khotilovich, V., & Tang, Y. (2019). Xgboost: Extreme gradient boosting. R package version, 0.90. 5. Huang, G., Cheng, Y., & Chen, C. (2018). Gradient boosting decision tree methods for high-dimensional classification and regression. Transactions on Intelligent Systems and Technology, 9(1), 1-24. 6. Li, T., Zhu, S., & Ogihara, M. (2018). Gradient boosting decision tree with random feature subspace and random instance subsampling. Neurocomputing, 275, 2073-2082. 7. Wang, J., Zhang, T., & Li, Y. (2018). Multi-view gradient boosting decision tree. In IJCAI (pp. 3410-3416). 8. Sun, Y., Liu, Y., Zhang, X., & Li, Z. (2020). Multi-branch gradient boosting decision tree for imbalanced data classification. Applied Soft Computing, 86, 105916. 9. Wang, M., Li, X., & Wang, Y. (2020). Gradient boosting decision tree based on optimal feature selection and parameter tuning. Expert Systems with Applications, 143, 113050. 10. Zhang, S., Zhou, J., & Zhang, P. (2020). Gradient boosting decision tree with adaptive learning rate and dropout regularization. Neurocomputing, 379, 118-126.

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