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

seudo-Zernike is a kind of region-based shape descriptor. The concept of the pseudo-Zernike moments were introduced, and their good characteristics, such as invariance, robustness and effectiveness, were discussed. Images could be reconstructed based on invariant pseudo-Zernike moments. Experiment results demonstrate the feasibility of the image reconstruction based on the improved pseudo-Zernike moments.