.png){kind=icon}
返回
Entropy and Confidence-Based Undersampling Boosting Random Forests for Imbalanced Problems
.png){kind=icon}
PRE
AI
DOI：10.1109/TNNLS.2020.2964585.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 article, we propose a novel entropy and confidence-based undersampling boosting (ECUBoost) framework to solve imbalanced problems. The boosting-based ensemble is combined with a new undersampling method to improve the generalization performance. To avoid losing informative samples during the data preprocessing of the boosting-based ensemble, both confidence and entropy are used in ECUBoost as benchmarks to ensure the validity and structural distribution of the majority samples during the undersampling. Furthermore, different from other iterative dynamic resampling methods, ECUBoost based on confidence can be applied to algorithms without iterations such as decision trees. Meanwhile, random forests are used as base classifiers in ECUBoost. Furthermore, experimental results on both artificial data sets and KEEL data sets prove the effectiveness of the proposed method.
Keyword:
Entropy
Radio frequency
Boosting
Decision trees
Training
Random forests
Heuristic algorithms
Confidence
ensemble learning
entropy
imbalanced problems
random forests (RFs)
undersampling
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
8.9
论文数:
7.6K
被引数:
7.2W
机构
暂无机构信息
引用论文
Interactions between the nonionic surfactant and polyacrylamide studied by light scattering and viscometry.png)
Polymer
PolymerIF0
PREAI
分享收藏Scanning mass spectrometer for quantitative reaction studies on catalytically active microstructures
PREAI
分享收藏Cascade interpolation learning with double subspaces and confidence disturbance for imbalanced problemsNEURAL NETWORKS
NEURAL NETWORKSIF6.3
PREAI
分享收藏
分享收藏
查看更多内容