计算机工程与应用2025，Vol.61Issue(2)：59-72,14.DOI:10.3778/j.issn.1002-8331.2407-0160
不平衡数据流的集成分类方法综述
Ensemble Classification Methods for Imbalanced Data Streams
摘要
Abstract
In real-world scenarios,learning from data streams often faces the challenge of class imbalance,where learning algorithms are unable to effectively recognize minority class samples due to the lack of training data.To introduce the cur-rent research status and challenges of ensemble classification for imbalanced data streams,recent literature in this field is reviewed.The analysis and summary are conducted from the perspectives of decision rules based on weighting,selection,and voting,as well as learning methods based on cost-sensitive learning,active learning,and incremental learning.The performance of algorithms using the same dataset is compared.To address the imbalance issues in different types of com-plex data streams,ensemble classification algorithms are summarized from four aspects:concept drift,multi-class,noise,and class overlap.The time complexity of classical algorithms is analyzed.Finally,the classification challenges of imbal-anced issues in dynamic data streams,data streams with missing information,multi-label data streams,and uncertain data streams are proposed for future research on ensemble strategies.关键词
不平衡数据流/集成分类/决策规则/学习方式/复杂数据流Key words
imbalanced data streams/ensemble classification/decision rule/learning methods/complex data streams分类
信息技术与安全科学引用本文复制引用
朱诗能,韩萌,杨书蓉,代震龙,杨文艳,丁剑..不平衡数据流的集成分类方法综述[J].计算机工程与应用,2025,61(2):59-72,14.基金项目
国家自然科学基金(62062004) （62062004）
宁夏自然科学基金(2022AAC03279). （2022AAC03279）
