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Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/22412
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dc.contributor.authorLai, CS-
dc.contributor.authorTao, Y-
dc.contributor.authorXu, F-
dc.contributor.authorNg, WWY-
dc.contributor.authorJia, Y-
dc.contributor.authorYuan, H-
dc.contributor.authorHuang, C-
dc.contributor.authorLai, LL-
dc.contributor.authorXu, Z-
dc.contributor.authorLocatelli, G-
dc.date.accessioned2021-03-13T11:41:27Z-
dc.date.available2018-08-16-
dc.date.available2021-03-13T11:41:27Z-
dc.date.issued2019-01-01-
dc.identifier.citationLai, C.S., Tao, Y., Xu, F., Ng, W.W.Y., Jia, Y., Yuan, H., Huang, C., Lai, L.L., Xu, Z. and Locatelli, G. (2019) 'A robust correlation analysis framework for imbalanced and dichotomous data with uncertainty', Information Sciences, 470, pp. 58 - 77. doi: https://doi.org/10.1016/j.ins.2018.08.017.en_US
dc.identifier.issn0020-0255-
dc.identifier.urihttps://bura.brunel.ac.uk/handle/2438/22412-
dc.description.sponsorshipGuangdong University of Technology, Guangzhou, China under Grant from the Financial and Education Department of Guangdong Province 2016[202]: Key Discipline Construction Programme; the Education Department of Guangdong Province: New and Integrated Energy System Theory and Technology Research Group, Project Number 2016KCXTD022 and National Natural Science Foundation of China under Grant Number 61572201.en_US
dc.format.extent58 - 77-
dc.format.mediumPrint-Electronic-
dc.languageEnglish-
dc.language.isoen_USen_US
dc.publisherElsevieren_US
dc.subjectPearson product-moment correlationen_US
dc.subjectimbalanced dataen_US
dc.subjectclearness indexen_US
dc.subjectdichotomous variableen_US
dc.titleA robust correlation analysis framework for imbalanced and dichotomous data with uncertaintyen_US
dc.typeArticleen_US
dc.identifier.doihttps://doi.org/10.1016/j.ins.2018.08.017-
dc.relation.isPartOfInformation Sciences-
pubs.publication-statusPublished-
pubs.volume470-
dc.identifier.eissn1872-6291-
Appears in Collections:Dept of Electronic and Electrical Engineering Research Papers

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