关键词: 数据流, 概念漂移, 集成分类器, 分类

Abstract: The tracking of drifting concept from data streams has recently become one of hot spots in data mining.In this paper，a Example-Weighted algorithm for mining data streams （EWAMDS） is proposed for data streams classification in the presence of concept drift，in which weight of train example is adjusted according to base classifier’s prediction on it，so as to enhance influence of drifting examples in new classifier，and a dynamic weight modifying factor is introduced to improve the adaptability of this algorithm.The results of experiments indicate that modifying weight of example dynamically makes this algorithm more adaptively；and in comparison with weighted-bagging，EWAMDS has a lower time consumption and higher accuracy.

Key words: data streams, concept drifts, ensemble classifier, classification