Efficient Ensemble Classification for Multi-Label Data Streams with Concept Drift

Information

卷:10

Yange Sun¹  Shasha Wang¹  Han Shao¹ 

[1] School of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, China;

关键词: data streams;    multi-label;    concept drift;    ensemble classification;    label dependency;   

DOI  :  10.3390/info10050158

来源: DOAJ

【 摘 要 】

Most existing multi-label data streams classification methods focus on extending single-label streams classification approaches to multi-label cases, without considering the special characteristics of multi-label stream data, such as label dependency, concept drift, and recurrent concepts. Motivated by these challenges, we devise an efficient ensemble paradigm for multi-label data streams classification. The algorithm deploys a novel change detection based on Jensen−Shannon divergence to identify different kinds of concept drift in data streams. Moreover, our method tries to consider label dependency by pruning away infrequent label combinations to enhance classification performance. Empirical results on both synthetic and real-world datasets have demonstrated its effectiveness.

【 授权许可】

Unknown