.png){kind=icon}
返回
Data-driven multinomial random forest: a new random forest variant with strong consistency
.png){kind=icon}
OA
AI
DOI：10.1186/s40537-023-00874-6.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper, we modify the proof methods of some previously weakly consistent variants of random forest into strongly consistent proof methods, and improve the data utilization of these variants in order to obtain better theoretical properties and experimental performance. In addition, we propose the Data-driven Multinomial Random Forest (DMRF) algorithm, which has the same complexity with BreimanRF (proposed by Breiman) while satisfying strong consistency with probability 1. It has better performance in classification and regression tasks than previous RF variants that only satisfy weak consistency, and in most cases even surpasses BreimanRF in classification tasks. To the best of our knowledge, DMRF is currently a low-complexity and high-performing variation of random forest that achieves strong consistency with probability 1.
Keyword:
Random forest
Strong consistency
Classification
Regression
Machine learning
期刊
IF:
6.4
论文数:
1.5K
被引数:
1.1W
机构
引用论文
Newer classification and regression tree techniques: Bagging and random forests for ecological prediction.png)
ECOSYSTEMS
ECOSYSTEMSIF3.3
PREAI
分享收藏分享收藏
查看更多内容