基于子空间集成的概念漂移数据流分类算法

Classification Algorithm for Concept-Drifting Data Stream Based on Subspace Integration

具有概念漂移的复杂结构数据流分类问题已成为数据挖掘领域研究的热点之一。提出了一种新颖的子空间分类算法,并采用层次结构将其构成集成分类器用于解决带概念漂移的数据流的分类问题。在将数据流划分为数据块后,在每个数据块上利用子空间分类算法建立若干个底层分类器,然后由这几个底层分类器组成集成分类模型的基分类器。同时,引入数理统计中的参数估计方法检测概念漂移,动态调整模型。实验结果表明:该子空间集成算法不但能够提高分类模型对复杂类别结构数据流的分类精度,而且还能够快速适应概念漂移的情况。

The classification of concept-drifting data streams with complex category structures has recently becomes one of the most popular topics in data mining. This paper proposes a novel subspace classification method, and uses it to form an ensemble classifier in a hierarchical structure for concept-drifting data streams classification. After dividing a given data stream into several data blocks, it uses the subspace classification method to train some bottom classifiers on each data block, and then uses these bottom classifiers to form a base classifier. The base classifers are used to build the ensemble classifier. Meanwhile, it introduces the parameter estimation method to detect concept drift. Experimental results show that the proposed method does not only significantly improve the classification performance on datasets with complex category structures, but also quickly adapts to the situation of concept drift.