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

摘要： 数据流上的漂移概念发现已成为数据挖掘领域的研究热点之一。针对存在概念漂移的数据流分类问题，提出一种基于实例加权方法的数据流分类算法（EWAMDS），根据基分类器在训练实例上的分类结果调整该实例的权值，以增强漂移实例在新分类器中的影响，同时引入动态的权值修改因子以提高算法的适应性。实验结果表明，动态地调整实例的权值时算法的适应性更强；与weighted-bagging相比，EWAMDS的时间开销显著降低、分类正确率显著提高。

关键词: 数据流, 概念漂移, 集成分类器, 分类