Reduced-space multistream classification based on multi-objective evolutionary optimization

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dc.cclicenceCC BYen
dc.contributor.authorJiao, Botao
dc.contributor.authorGuo, Yinan
dc.contributor.authorYang, Shengxiang
dc.contributor.authorPu, Jiayang
dc.contributor.authorGong, Dunwei
dc.date.acceptance2022-10
dc.date.accessioned2023-05-12T08:13:15Z
dc.date.available2023-05-12T08:13:15Z
dc.date.issued2022-12-27
dc.description.abstractIn traditional data stream mining, classification models are typically trained on labeled samples from a single source. However, in real-world scenarios, obtaining accurate labels is very hard and expensive, especially when multiple data streams are concurrently sampled from an environment or the same process. To address this issue, multistream classification is proposed, in which a data stream with biased labels (called the source stream) is leveraged to train a suitable model for prediction over another stream with unlabeled samples (called the target stream). Despite the growing research in this field, previous multistream classification methods are mostly designed for single source stream scenarios. However, various source streams contain diverse data distributions, providing more valuable information for building a more accurate model. In addition, previous works construct classification models in the original shared feature space, ignoring the effect of redundant or low-quality features on the classification performance. This may produce inefficient knowledge transfer across streams. In view of this, a reduced-space multistream classification based on multi-objective evolutionary optimization is proposed in this paper. First, a multi-objective evolutionary optimization is employed to seek the most valuable feature subset shared in the source and target domains, with the purpose of narrowing the distribution difference between source and target streams. Following that, a Gaussian Mixture Model-based weighting mechanism for source samples is presented. More especially, two drift adaptation methods are proposed to address asynchronous drift. Experimental results on benchmark datasets show that the proposed method outperforms other comparative methods on classification accuracy and G-mean.en
dc.exception.reasonArticle not deposited within 3 months of publication.en
dc.funderOther external funder (please detail below)en
dc.funder.otherNational Key Research and Development Program of Chinaen
dc.funder.otherNational Natural Science Foundation of Chinaen
dc.identifier.citationJiao, B., Guo, Y., Yang, S., Pu, J. and Gong, D. (2023) Reduced-space multistream classification based on multi-objective evolutionary optimization. IEEE Transactions on Evolutionary Computation,en
dc.identifier.doihttps://doi.org/10.1109/TEVC.2022.3232466
dc.identifier.issn1941-0026
dc.identifier.urihttps://hdl.handle.net/2086/22922
dc.language.isoen_USen
dc.peerreviewedYesen
dc.projectid2022YFB4703701en
dc.projectid61973305, 61573361, 52121003en
dc.publisherIEEEen
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en
dc.subjectConcept driften
dc.subjectmultistream classificationen
dc.subjectdomain adaptionen
dc.subjectfeature selectionen
dc.subjectmulti-objective optimizationen
dc.titleReduced-space multistream classification based on multi-objective evolutionary optimizationen
dc.typeArticleen

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