计算机技术与发展2025，Vol.35Issue(3)：103-108,6.DOI:10.20165/j.cnki.ISSN1673-629X.2024.0354
基于双缓冲区的概念漂移检测方法
Concept Drift Detection Method Based on Double Buffers
摘要
Abstract
In data analysis,the issue of concept drift occurs frequently,which causes models to fail to adapt to the dynamic changes in data distribution.Research has been conducted on how to handle concept drift in data streams in order to improve data analysis performance.Therefore,based on the combination of Online Sequential Extreme Learning Machine(OS-ELM)and Drift Detection Method(DDM-OS-ELM),a Double-Buffer approach(Buffer A and Buffer B)is proposed.DDM-OS-ELM deals with concept drift by combining drift detection mechanism and online sequential extreme learning machine.This method triggers model update when concept drift is detected.In the process of detection,the problem of concept drift is solved by double buffers.Buffer A addresses the problem of insufficient data for retraining the model after a concept drift occurs.Buffer B collects data after the concept drift,allowing the model to adapt to the new data distribution.Experimental results show that the use of Double Buffers not only reduces the number of model updates but also improves the accuracy of model predictions.关键词
概念漂移/双缓冲区/在线序列极限学习机/漂移检测机制/不确定数据流Key words
concept drift/double-buffer/online sequential extreme learning machine/drift detection mechanism/uncertain data streams分类
计算机与自动化引用本文复制引用
李盟,温伍正宏,潘甦..基于双缓冲区的概念漂移检测方法[J].计算机技术与发展,2025,35(3):103-108,6.基金项目
国家自然科学基金(62071244) （62071244）
