中文摘要：

针对隐含概念漂移和噪声的数据流,提出一种基于模糊积分融合的数据流分类方法（fuzzy integral ensembleclassifiers for mining data streams,FI-MDS）。将模糊积分融合方法与集成综合技术有效结合起来,首先通过基分类器对识别样例进行分类得到决策剖面,然后再用模糊积分融合方法得到最终的分类结果,同时引入动态权值更新以提高算法的适应性。实验结果表明,与传统的数据流分类算法相比,该方法提高了概念漂移的检测精度,有效地解决了数据流中复杂分类问题,具有良好的分类性和健壮性。

英文摘要：

A new classification algorithm FI-MDS based on fuzzy integral fusion was proposed,which aimed at mining data steams with concept drifts and noise and combined fuzzy integral fusion and ensemble multi-classifiers technology.First,the decision-making profile was obtained by training samples through base classifiers,and then the final classification result was obtained via fuzzy integral fusion.Also,a dynamic weight update was introduced to improve the adaptability of this algorithm.Experimental results indicated that this method could enhance the detection accuracy of the concept drifts.Complex classification problems in data streams could be solved and the algorithm has higher classification performance,effectiveness and robustness.