基于图模型的数据流分类算法

Research on Data Stream Classification Algorithm Based on Graph Model

针对数据流环境中混合多种类型概念漂移问题,提出了基于图模型的数据流分类算法.该算法通过对数据块上的实例集进行概念表示,检测概念的变化度来衡量概念漂移,并引入了一个动态自适应阈值,为每个待分类实例合理选择基分类器模型,充分利用基分类器模型潜在的多样性并降低漂移恢复期间的分类误差.实验表明,本文提出的算法性能在多数数据集上优于其他算法,在复杂概念漂移环境下具有较好的适应性.

Based on concept transfer graph model,a data stream classification algorithm is proposed and designed to deal with mixed concept drift environment.The algorithm measures the concept drift by conceptual representation of the instance set on the data block,detecting the degree of change of the concept,and introduces a dynamic and adaptive threshold.The base classifier model is reasonably selected for each instance to be classified,making full use of the potential diversity of the base classifier model and reducing the classification error during drift recovery.The performance of the proposed algorithm is superior to other algorithms on most of data sets,and has good adaptability in complex concept drift environments.