关键词: 概念漂移, 数据流, 异构欧氏距离, 交叠数据窗

Abstract: To solve the false detection and detection delay of concept drift for data stream, a new online concept drift detection method based on the distance measurement of overlapped data windows was proposed in this paper. By dividing the data stream into overlapped data windows and computing the heterogeneous Euclidean distance of neighboring windows, and measuring the inconsistency of the data windows through the nearest neighbor principle, the authors could achieve the evaluation of distribution diversity and the detection of concept drift. To evaluate the effectiveness of the proposed method, experiments were made on some public data sets with different drift severity and drift speed. The experimental results show that the proposed method can detect different types of concept drift quickly and accurately and can figure out the locations where concept drift appeared. Key words: concept drift; data stream; heterogeneous Euclidean distance; overlap data windows

Key words: concept drift, data stream, heterogeneous Euclidean distance, overlap data window