概念漂移检测与适应方法综述OA北大核心CSTPCD

With the rapid growth of data information,the characteristics of data stream are diversified,which put forward new requirements for the study of concept drift processing methods.The previous detection ideas based on performance degradation cannot cope with the non-instant availability environment of labels,which promotes the research of un-labeled detection methods.The research of ensemble and incremental learning promotes the progress of drift adaptation method.Firstly,the drift detection methods are analyzed in detail in two cases,namely whether the label is available and immedi-ate or not,and based on the model composition,the drift adaptation methods are analyzed and explained with emphasis on the ensemble method.Secondly,the drift processing methods of different ideas are comprehensively summarized from the core ideas,comparison models,advantages and limitations,and the drift processing methods of different ideas are com-pared as a whole.Finally,the future research directions in this field are given,including noise discrimination,threshold setting and criterion diversification.