{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T16:47:02Z","timestamp":1762015622596,"version":"build-2065373602"},"reference-count":36,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2019,4,28]],"date-time":"2019-04-28T00:00:00Z","timestamp":1556409600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>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\u2013Shannon 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.<\/jats:p>","DOI":"10.3390\/info10050158","type":"journal-article","created":{"date-parts":[[2019,4,29]],"date-time":"2019-04-29T02:57:32Z","timestamp":1556506652000},"page":"158","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Efficient Ensemble Classification for Multi-Label Data Streams with Concept Drift"],"prefix":"10.3390","volume":"10","author":[{"given":"Yange","family":"Sun","sequence":"first","affiliation":[{"name":"School of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, China"},{"name":"School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China"},{"name":"Henan Key Lab of Analysis and Applications of Education Big Data, Xinyang Normal University, Xinyang 464000, China"}]},{"given":"Han","family":"Shao","sequence":"additional","affiliation":[{"name":"School of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, China"}]},{"given":"Shasha","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,4,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"344","DOI":"10.1016\/j.inffus.2005.05.005","article-title":"Real-time data mining of non-stationary data streams from sensor networks","volume":"9","author":"Cohen","year":"2008","journal-title":"Inf. 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