Semi-Supervised Classification of Data Streams by BIRCH Ensemble and Local Structure Mapping

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.