A novel semi-supervised classification approach for evolving data streams

Classification plays a crucial role in mining the evolving data streams. The concept drift and concept evolution are the major issues of data streams classification, which greatly affect the classification performance. Most existing works of concept drift and evolution are supervised in nature, where labeling the data is time and resource consuming. In this paper, for the evolving data streams, a semi-supervised classification approach using partially labeled data is proposed. Firstly, an ensemble model dynamically maintains a series of micro -clusters to capture the concept drift. The ensemble model processes the instances in an online fashion rather than chunk-based. Secondly, the concept evolution detection module is constructed to detect the outliers by the local density. The module examines the current buffer to capture the class emergence in data streams with complex class distribution. For improving the execution efficiency of emerging class detection without compromising performance, several constructive strategies are adopted, including removing part of the buffer and selectively executing sample generation. The extensive experiments are constructed about the popular data streams sets and the processed industry data streams, whose results indicate the practicality and effectiveness of the proposed approach for the classification of evolving data streams.