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

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...

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Bibliographic Details
Published in:Information (Basel) 2019, Vol.10 (5), p.158
Main Authors: Sun, Yange, Shao, Han, Wang, Shasha
Format: Article
Language:English
Subjects:
Algorithms
Classification
concept drift
data streams
Data transmission
Drift
ensemble classification
label dependency
Methods
multi-label
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Summary: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.
ISSN:2078-2489
2078-2489
DOI:10.3390/info10050158