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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Main Authors:	Yange Sun, Han Shao, Shasha Wang
Format:	Article
Language:	English
Published:	MDPI AG 2019-04-01
Series:	Information
Subjects:	data streams multi-label concept drift ensemble classification label dependency
Online Access:	https://www.mdpi.com/2078-2489/10/5/158

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record_format	Article
spelling	doaj-40e44c4cf91a430b9b55fa6137e86e7d2020-11-25T01:36:36ZengMDPI AGInformation2078-24892019-04-0110515810.3390/info10050158info10050158Efficient Ensemble Classification for Multi-Label Data Streams with Concept DriftYange Sun0Han Shao1Shasha Wang2School of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, ChinaSchool of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, ChinaSchool of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, ChinaMost 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.https://www.mdpi.com/2078-2489/10/5/158data streamsmulti-labelconcept driftensemble classificationlabel dependency
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Yange Sun Han Shao Shasha Wang
spellingShingle	Yange Sun Han Shao Shasha Wang Efficient Ensemble Classification for Multi-Label Data Streams with Concept Drift Information data streams multi-label concept drift ensemble classification label dependency
author_facet	Yange Sun Han Shao Shasha Wang
author_sort	Yange Sun
title	Efficient Ensemble Classification for Multi-Label Data Streams with Concept Drift
title_short	Efficient Ensemble Classification for Multi-Label Data Streams with Concept Drift
title_full	Efficient Ensemble Classification for Multi-Label Data Streams with Concept Drift
title_fullStr	Efficient Ensemble Classification for Multi-Label Data Streams with Concept Drift
title_full_unstemmed	Efficient Ensemble Classification for Multi-Label Data Streams with Concept Drift
title_sort	efficient ensemble classification for multi-label data streams with concept drift
publisher	MDPI AG
series	Information
issn	2078-2489
publishDate	2019-04-01
description	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.
topic	data streams multi-label concept drift ensemble classification label dependency
url	https://www.mdpi.com/2078-2489/10/5/158
work_keys_str_mv	AT yangesun efficientensembleclassificationformultilabeldatastreamswithconceptdrift AT hanshao efficientensembleclassificationformultilabeldatastreamswithconceptdrift AT shashawang efficientensembleclassificationformultilabeldatastreamswithconceptdrift
_version_	1725062032729833472

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

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