计算机工程与应用2025，Vol.61Issue(10)：79-95,17.DOI:10.3778/j.issn.1002-8331.2406-0107
基于加权与动态选择的不平衡数据流分类算法
Imbalanced Stream Classification Algorithm Based on Weighted and Dynamic Selection
摘要
Abstract
In the field of data mining,data stream mining is a critical task aimed at processing continuously generated and evolving data streams.Unlike traditional batch data mining,data stream mining emphasizes real-time data processing and analysis,offering higher timeliness and practicality.However,real-world data streams present practical challenges such as multi-class imbalance,varying class imbalance ratios,and concept drift,which can significantly degrade classifier perfor-mance.To address these issues,an imbalanced stream classification algorithm based on weighted and dynamic selection(SDW-DES)is proposed.This algorithm provides a reliable solution for real-time applications by comprehensively consid-ering sample difficulty and data dynamics.A weighting strategy based on sample classification difficulty is introduced,which incorporates margin values and Focal Loss to more effectively focus on easily misclassified samples and minority class samples,thereby improving classifier accuracy.A flexible dynamic ensemble selection method is proposed,which utilizes sample sliding windows and hard sample sliding windows to comprehensively analyze classifier performance across different windows.This method assigns weights and selects the best classifiers for ensemble prediction to adapt to the dynamic changes in data distribution.A comprehensive experimental evaluation is conducted on various data stream environments and evaluation metrics,SDW-DES is compared with 9 advanced algorithms.The experimental results demon-strate that SDW-DES achieves the highest average ranking across 4 evaluation metrics,and possess superior adaptability to the challenges of imbalance and concept drift in data streams.关键词
数据流分类/多类不平衡/概念漂移/样本加权/动态集成选择Key words
data stream classification/multi-class imbalance/concept drift/sample weighting/dynamic ensemble selection分类
信息技术与安全科学引用本文复制引用
韩萌,李春鹏,李昂,孟凡兴,何菲菲,张瑞华..基于加权与动态选择的不平衡数据流分类算法[J].计算机工程与应用,2025,61(10):79-95,17.基金项目
国家自然科学基金(62062004) （62062004）
宁夏自然科学基金(2022AAC03279) （2022AAC03279）
北方民族大学中央高校基本科研业务费专项资金(2021KJCX10). （2021KJCX10）
