Abstract: Data streams mining has become one of hot topics in the area of data mining.Because of the existence of concept drift,it is impossible for conventional classification algorithms to be directly applied in data streams environment.In order to deal with the concept changes in data streams,an algorithm based on Kappa coefficient was proposed.The approach uses ensemble classification techniques and a weighted voting strategy to decide the labels of test sets,in addition,the approach employs Kappa coefficient to measure the performance of classification system.When the performance of classifiers decreases significantly,an alarm about concept drift will be made and the algorithm will apply prior know-ledge to delete inaccurate classifiers to adapt to new concept.The experimental results shows that,comparing with the contrast algorithms in the experiments:BWE,AE and AWE,the new approach can not only possess better performance for classification,but also efficiently decrease time cost.

Key words: Data streams,Concept drift,Classification,Kappa coefficient