.png){kind=icon}
返回
Diversified Sensitivity-Based Undersampling for Imbalance Classification Problems
.png){kind=icon}
PRE
AI
DOI：10.1109/TCYB.2014.2372060.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Undersampling is a widely adopted method to deal with imbalance pattern classification problems. Current methods mainly depend on either random resampling on the majority class or resampling at the decision boundary. Random-based under-sampling fails to take into consideration informative samples in the data while resampling at the decision boundary is sensitive to class overlapping. Both techniques ignore the distribution information of the training dataset. In this paper, we propose a diversified sensitivity-based undersampling method. Samples of the majority class are clustered to capture the distribution information and enhance the diversity of the resampling. A stochastic sensitivity measure is applied to select samples from both clusters of the majority class and the minority class. By iteratively clustering and sampling, a balanced set of samples yielding high classifier sensitivity is selected. The proposed method yields a good generalization capability for 14 UCI datasets.
Keyword:
Diversified sensitivity undersampling (DSUS)
imbalance data
sample selection
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:
10.5
论文数:
1.1W
被引数:
5.0W
机构
引用论文
Scanning mass spectrometer for quantitative reaction studies on catalytically active microstructures
PREAI
分享收藏Training cost-sensitive neural networks with methods addressing the class imbalance problem用解决类不平衡问题的方法训练代价敏感的神经网络
PREAI
分享收藏Significant Rewiring of the Transcriptome and Proteome of an Escherichia coli Strain Harboring a Tailored Exogenous Global Regulator IrrE.png)
PLoS ONE
OAAI
PLoS ONEIF0
OAAI分享收藏Arsenic removal from aqueous solutions by adsorption using novel MIL-53(Fe) as a highly efficient adsorbent使用新型MIL-53(Fe) 作为高效吸附剂通过吸附从水溶液中去除砷RSC Advances
RSC AdvancesIF0
PREAI
分享收藏On the effectiveness of preprocessing methods when dealing with different levels of class imbalance
PREAI
分享收藏
查看更多内容