文章基本信息

标题：Efficient Ensemble Classification for Multi-Label Data Streams with Concept Drift
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作者：Yange Sun ; Han Shao ; Shasha Wang 等
期刊名称：Information
电子版ISSN：2078-2489
出版年度：2019
卷号：10
期号：5
页码：158-171
DOI：10.3390/info10050158
出版社：MDPI Publishing
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.
关键词：data streams; multi-label; concept drift; ensemble classification; label dependency data streams ; multi-label ; concept drift ; ensemble classification ; label dependency