.png){kind=icon}
返回
Perturbation-based oversampling technique for imbalanced classification problems
.png){kind=icon}
PRE
AI
DOI：10.1007/s13042-022-01662-z.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
We present a simple yet effective idea, perturbation-based oversampling (POS), to tackle imbalanced classification problems. In this method, we perturb each feature of a given minority instance to generate a new instance. The originality and advantage of the POS is that a hyperparameter p is introduced to control the variance of the perturbation, which provides flexibility to adapt the algorithm to data with different characteristics. Experimental results yielded by using five types of classifiers and 11 performance metrics on 103 imbalanced datasets show that the POS offers comparable or better results than those yielded by 11 reference methods in terms of multiple performance metrics. An important finding of this work is that a simple perturbation-based oversampling method is able to yield better classification results than many advanced oversampling methods by controlling the variance of input perturbation. This reminds us it may need to conduct comparisons with simple oversampling methods, e.g., POS, when designing new oversampling approaches.
Keyword:
Class imbalance
Learning
Oversampling
Perturbation
期刊
IF:
2.7
论文数:
3.2K
被引数:
5.6K
机构
引用论文
KNN-based maximum margin and minimum volume hyper-sphere machine for imbalanced data classification
PREAI
分享收藏RWO-Sampling: A random walk over-sampling approach to imbalanced data classificationRWO抽样: 一种非平衡数据分类的随机游走过抽样方法.png)
INFORMATION FUSION
INFORMATION FUSIONIF15.5
PREAI
分享收藏
查看更多内容