Study on the classification of data streams with concept drift

Abstract

Data streams mining has become a novel research topic of growing interest in knowledge discovery. Because of the high speed and huge size of data set in data streams, the traditional classification technologies are no longer applicable. In recent years a great deal of research has been done on this problem, most intends to efficiently solve the data streams mining problem with concept drift. This paper presents the state-of-the-art in this field with growing vitality and introduces the methods for detecting concept drift in data stream, then gives a critical summary of existing approaches to the problem, including Stagger, FLORA, MetaL(B), MetaL(IB), CD3, CD4, CD5, OLIN, CVFDT and different ensemble classifiers. At last, this paper explores the challenges and future work in this field.