Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/164678
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dc.contributor.authorZhang, Xiaocaien_US
dc.contributor.authorPeng, Huien_US
dc.contributor.authorZhang, Jianjiaen_US
dc.contributor.authorWang, Yangen_US
dc.date.accessioned2023-02-08T06:57:15Z-
dc.date.available2023-02-08T06:57:15Z-
dc.date.issued2023-
dc.identifier.citationZhang, X., Peng, H., Zhang, J. & Wang, Y. (2023). A cost-sensitive attention temporal convolutional network based on adaptive top-k differential evolution for imbalanced time-series classification. Expert Systems With Applications, 213, 119073-. https://dx.doi.org/10.1016/j.eswa.2022.119073en_US
dc.identifier.issn0957-4174en_US
dc.identifier.urihttps://hdl.handle.net/10356/164678-
dc.description.abstractImbalanced time-series classification (ITSC) is ubiquitous in many real-world applications. In this study, a novel cost-sensitive deep learning framework, namely ACS-ATCN, is proposed for ITSC. With the framework of ACS-ATCN, first, weighted class costs are optimized jointly with the hyperparameters of an attention temporal convolutional network (ATCN). Second, an improved evolutionary algorithm, termed adaptive top-k differential evolution (ATDE), is presented for optimizing class costs as well as the network's hyperparameter. Experiments on five data sets demonstrate that ACS-ATCN achieves a higher average G-mean than other cost-sensitive learning and oversampling algorithms while using much less computational time. Comparison between different deep learning frameworks also confirms its advantages over other existing benchmarking methods in ITSC. Experimental results also reveal that ATDE provides more accurate classification than the vanilla DE algorithm, and saves as high as 41.53% of average computational expense for convergence.en_US
dc.language.isoenen_US
dc.relation.ispartofExpert Systems with Applicationsen_US
dc.rights© 2022 Elsevier Ltd. All rights reserved.en_US
dc.subjectScience::Biological sciencesen_US
dc.titleA cost-sensitive attention temporal convolutional network based on adaptive top-k differential evolution for imbalanced time-series classificationen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Biological Sciencesen_US
dc.identifier.doi10.1016/j.eswa.2022.119073-
dc.identifier.scopus2-s2.0-85140923444-
dc.identifier.volume213en_US
dc.identifier.spage119073en_US
dc.subject.keywordsImbalanceden_US
dc.subject.keywordsTime-Series Classificationen_US
dc.description.acknowledgementThis work was supported in part by National Natural Science Foundation of China (grant number 62101611) and Natural Science Foundation of Guangdong Province (grant number 2022A1515011375).en_US
item.grantfulltextnone-
item.fulltextNo Fulltext-
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