.png){kind=icon}
返回
An Evolutionary Forest for Regression
.png){kind=icon}
PRE
AI
DOI：10.1109/TEVC.2021.3136667.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Random forest (RF) is a type of ensemble-based machine learning method that has been applied to a variety of machine learning tasks in recent years. This article proposes an evolutionary approach to generate an oblique RF for regression problems. More specifically, our method induces an oblique RF by transforming the original feature space to a new feature space through the evolutionary feature construction method. To speed up the searching process, the proposed method evaluates each set of features based on a decision tree (DT) rather than an RF. In order to obtain an RF, we archive top-performing features and corresponding trees during the search. In this way, both the features and the forest can be constructed simultaneously in a single run. The proposed evolutionary forest is applied to 117 benchmark problems with different characteristics and compared with some state-of-the-art regression methods, including several variants of the RF and gradient boosted DTs (GBDTs). The experimental results suggest that the proposed method outperforms the existing RF and GBDT methods.
Keyword:
Random forests
Decision trees
Forestry
Machine learning algorithms
Regression tree analysis
Optimization
Boosting
Evolutionary feature construction
evolutionary forest (EF)
genetic programming (GP)
random forest (RF)
期刊
IF:
12
论文数:
1.8K
被引数:
2.4W
机构
引用论文
分享收藏Meso-Mechanical Characteristics of Granite with Natural Cracks after Mud Acid Corrosion.png)
Energies
OAAI
EnergiesIF0
OAAI分享收藏Entstehung eines Stachelzell-karzinoms auf einem chronischen Analekzem und therapeutische KonsequenzenDer Hautarzt
Der HautarztIF0
PREAI
分享收藏
查看更多内容