Machine learningMachine learning
正则化随机森林
由 Deng 和 Runger 于 2012 年提出的正则化随机森林 (Regularized Random Forest, RRF) 通过增加一个惩罚项来扩展标准随机森林，该惩罚项会抑制在集成中尚未使用的特征上的分裂。这种内置的正则化能够产生更稀疏、冗余度更低的的特征子集，使得该模型在特征选择与预测精度同等重要时尤为有价值。
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来源
- Deng, H., & Runger, G. (2012). Feature selection via regularized trees. Proceedings of the 2012 International Joint Conference on Neural Networks (IJCNN), IEEE, pp. 1–8. DOI: 10.1109/IJCNN.2012.6252640 ↗
- Deng, H., & Runger, G. (2013). Gene selection with guided regularized random forest. Pattern Recognition, 46(12), 3483–3489. DOI: 10.1016/j.patcog.2013.05.018 ↗
如何引用本页
ScholarGate. (2026, June 3). Regularized Random Forest (RRF). ScholarGate. https://scholargate.app/zh/machine-learning/regularized-random-forest
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
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