Many researchers have applied clustering to handle semi-supervised classification of data streams with concept drifts. However, the generalization ability for each specific concept cannot be steadily improved, and the concept drift detection method without considering the local structural information of data cannot accurately detect concept drifts. This paper proposes to solve these problems by BIRCH (Balanced Iterative Reducing and Clustering Using Hierarchies) ensemble and local structure mapping. The local structure mapping strategy is utilized to compute local similarity around each sample and combined with semi-supervised Bayesian method to perform concept detection. If a recurrent concept is detected, a historical BIRCH ensemble classifier is selected to be incrementally updated; otherwise a new BIRCH ensemble classifier is constructed and added into the classifier pool. The extensive experiments on several synthetic and real datasets demonstrate the advantage of the proposed algorithm.

中文翻译：

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通过 BIRCH 集成和局部结构映射对数据流进行半监督分类

许多研究人员已经应用聚类来处理具有概念漂移的数据流的半监督分类。然而，对于每个特定概念的泛化能力无法稳步提升，不考虑数据局部结构信息的概念漂移检测方法无法准确检测概念漂移。本文提出通过BIRCH（Balanced Iterative Reducing and Clustering Using Hierarchies）集成和局部结构映射来解决这些问题。利用局部结构映射策略计算每个样本周围的局部相似度，并结合半监督贝叶斯方法进行概念检测。如果检测到循环概念，则选择历史BIRCH集成分类器进行增量更新；否则，将构建一个新的 BIRCH 集成分类器并将其添加到分类器池中。在几个合成和真实数据集上的大量实验证明了所提出算法的优势。