.png){kind=icon}
返回
Global-local information based oversampling for multi-class imbalanced data
.png){kind=icon}
PRE
AI
DOI：10.1007/s13042-022-01746-w.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Multi-class imbalanced classification is a challenging problem in the field of machine learning. Many methods have been proposed to deal with it, and oversampling is one of the most popular techniques which alleviates class imbalance by generating instances for the minority classes. However, each oversampling utilizes a single way to generate instances for all candidate minority ones, which neglects the intrinsic characteristics among different minority class instances, and makes the synthetic instances redundant or ineffective. In this work, we propose a global-local-based oversampling method, termed GLOS. We introduce a new discreteness-based metric (DID) and distinguish the minority class from the majority class by comparing it with each class-level discreteness value. Then, for each minority class, some difficult-to-learn instances are selected, which have smaller instance-level dispersion than the corresponding class-level one, to generate synthetic instances. And the number of synthetic instances equals the difference between two types of dispersion values. These selected instances are assigned into different groups according to their local distribution. Furthermore, GLOS adopts a specific synthetic strategy to each group instance purposefully. Finally, all minority classes, part of the majority classes instances, and synthetic data will be used as training data. In this way, the quantity and quality of synthetic instances are guaranteed. Experimental results on KEEL and UCI data sets demonstrate the effectiveness of our proposal.
Keyword:
Oversampling
Intrinsic characteristics
Synthetic strategy
期刊
IF:
2.7
论文数:
3.2K
被引数:
5.6K
机构
引用论文
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.png)
RSC Advances
RSC AdvancesIF0
PREAI
分享收藏
查看更多内容