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Enhancing random forest algorithm performance using non-concave penalization techniques

delete2026-02-01
delete0
PRE
AI
Z
Zari Farhadi *
M
Mohammad‐Reza Feizi‐Derakhshi *
H
Hossein Bevrani
M
Mehdi Ebady Manaa
DOI：10.1080/00949655.2026.2632969delete
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摘要

摘要

En 中文
本文提出了一种名为稀疏剪枝随机森林（SPARF）的新型混合框架，该框架通过自动剪枝随机森林（RF）算法生成的树集成来提高预测精度。与传统的RF不同，所提出的框架采用非凸惩罚，即SCAD和GSCAD，以识别并消除冗余树。核心创新在于将基于SCAD的技术与RF集成，并聚合剩余的树。该模型利用SCAD和GSCAD的稀疏性，提供具有高预测精度的可解释模型。该方法在两个真实数据集和蒙特卡洛模拟中进行了评估，其中创建了一个结合RF、SCAD和GSCAD的模型以减少并自动选择RF树。在真实数据集中，SPARF-SC模型相较于RF的均方根误差（RMSE）降低了约9.68%，而SPARF-GSC模型则降低了约8.46%。
Keyword:
Machine learning
SCAD
group SCAD
shrinkage methods
random forest

期刊

J
Journal of Statistical Computation and Simulation
IF:
1.2
论文数:
133
被引数:
4.1K

机构

A
al-mustaqbal university college
学者数:
1.1K
论文数: 1.5K
被引数: 3
U
University of Tabriz
学者数:
9.3K
论文数: 8.5K
被引数: 1.0W
U
university of kurdistan
学者数:
309
论文数: 163
被引数: 0
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