软件导刊2025，Vol.24Issue(3)：43-47,5.DOI:10.11907/rjdk.241780
基于随机数据块与权重采样的不平衡分类集成算法
An Ensemble Algorithm Based on Random Patches and Weighted Sampling for Imbalanced Data Classification
魏勋1
作者信息
- 1. 江西理工大学 软件工程学院,江西 南昌 330000
- 折叠
摘要
Abstract
In many real-world problems,the datasets are typically imbalanced which probably degenerate the learning algorithm.To handle these skewed datasets,there are many class imbalance learning methods are proposed,especially ensemble methods due to their efficiency.While most of these ensemble methods mainly focus on the level of samples and neglect the features aspect.And conventional random sampling method do not pay enough attention to the boundary which always contain hard classified samples.Propose an ensemble sampling method named BRPE to overcome this deficiency.BRPE firstly samples a feature subset;then down-sample majority class instances via its closest eu-clidean distance to minority class samples to create a balanced random patch as training subset;then trains a base learner using each of sub-sets,and finally obtains the output combined of these learners.Experiments on both 10 synthetic datasets and 8 real-world datasets show that BRPE can achieve higher F1 and AUC values than other four existing ensemble methods for class imbalance.关键词
不平衡数据/类别不平衡学习/集成算法/权重采样/随机数据块Key words
imbalanced data/class imbalance learning/ensemble algorithm/weighted sampling/random patch分类
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魏勋..基于随机数据块与权重采样的不平衡分类集成算法[J].软件导刊,2025,24(3):43-47,5.
