2026-04-17T03:43:37Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/47492022-04-29T12:02:45Zcom_10259_4219com_10259_5086com_10259_2604col_10259_4220

Faithfull, William J. . d24391fc-a8aa-4ef1-a678-6c642db7b779 500 Rodríguez Diez, Juan José 477 500 Kuncheva, Ludmila I. . 37a0866d-eced-4e47-913c-cdc9943f0a48 500 2018-03-09T08:42:32Z 2019-01 2021-01 1566-2535 http://hdl.handle.net/10259/4749 10.1016/j.inffus.2018.02.003 Detecting change in multivariate data is a challenging problem, especially when class labels are not available. There is a large body of research on univariate change detection, notably in control charts developed originally for engineering applications. We evaluate univariate change detection approaches —including those in the MOA framework — built into ensembles where each member observes a feature in the input space of an unsupervised change detection problem. We present a comparison between the ensemble combinations and three established ‘pure’ multivariate approaches over 96 data sets, and a case study on the KDD Cup 1999 network intrusion detection dataset. We found that ensemble combination of univariate methods consistently outperformed multivariate methods on the four experimental metrics. project RPG-2015-188 funded by The Leverhulme Trust, UK; Spanish Ministry of Economy and Competitiveness through project TIN 2015-67534-P and the Spanish Ministry of Education, Culture and Sport through Mobility Grant PRX16/00495. The 96 datasets were originally curated for use in the work of Fernández-Delgado et al. [53] and accessed from the personal web page of the author5. The KDD Cup 1999 dataset used in the case study was accessed from the UCI Machine Learning Repository [10] application/pdf eng Elsevier Information Fusion. 2019, V. 45, p. 202-214 https://doi.org/10.1016/j.inffus.2018.02.003 info:eu-repo/grantAgreement/Leverhulme Trust/RPG-2015-188 info:eu-repo/grantAgreement/MINECO/TIN 2015-67534-P Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess Computer science Informática Combining univariate approaches for ensemble change detection in multivariate data info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion CC-LICENSE license_rdf license_rdf application/rdf+xml; charset=utf-8 805 https://riubu.ubu.es/bitstream/10259/4749/8/license_rdf 4460e5956bc1d1639be9ae6146a50347 MD5 8 THUMBNAIL Faithfull-IF_2019.pdf.jpg Faithfull-IF_2019.pdf.jpg IM Thumbnail image/jpeg 4069 https://riubu.ubu.es/bitstream/10259/4749/7/Faithfull-IF_2019.pdf.jpg 0455d5355b56d52b466e045e6bcadce3 MD5 7 ORIGINAL Faithfull-IF_2019.pdf Faithfull-IF_2019.pdf application/pdf 5119523 https://riubu.ubu.es/bitstream/10259/4749/1/Faithfull-IF_2019.pdf 2445dfdd9adf2494cdd735694f6652f6 MD5 1 LICENSE license.txt license.txt text/plain; charset=utf-8 999 https://riubu.ubu.es/bitstream/10259/4749/5/license.txt b295bcbce42e2caabeb0c623d3860c06 MD5 5 TEXT Faithfull-IF_2019.pdf.txt Faithfull-IF_2019.pdf.txt Extracted text text/plain 68566 https://riubu.ubu.es/bitstream/10259/4749/6/Faithfull-IF_2019.pdf.txt 626ce47cdab5a6740b40eb62f990b82e MD5 6 10259/4749 oai:riubu.ubu.es:10259/4749 2022-04-29 14:02:45.633 Repositorio Institucional de la Universidad de Burgos bubrep@ubu.es RWwgYXV0b3IgY29tbyDDum5pY28gdGl0dWxhciBkZSBsb3MgZGVyZWNob3MgZGUgcHJvcGllZGFkIGludGVsZWN0dWFsIGRlIGxhIG9icmEsIG8gZGlzcG9uaWVuZG8gZGUgbG9zIGRlYmlkb3MgcGVybWlzb3MgZGUgbG9zIG90cm9zIHRpdHVsYXJlcywgc2kgbG9zIGh1YmllcmEsIHkgZW4gdmlydHVkIGRlIGxvcyBkZXJlY2hvcyBxdWUgbGUgY29uZmllcmUgbGEgbGVnaXNsYWNpw7NuIHZpZ2VudGUgc29icmUgcHJvcGllZGFkIGludGVsZWN0dWFsIHkgZGVyZWNob3MgZGUgYXV0b3IsIA0KQVVUT1JJWkEgYSBsYSBVbml2ZXJzaWRhZCBkZSBCdXJnb3MgYSBkaWZ1bmRpciwgZGUgbWFuZXJhIGdyYXR1aXRhLCBlbCBjb250ZW5pZG8gZGUgbG9zIGFyY2hpdm9zIGRpZ2l0YWxlcyBxdWUgY29ycmVzcG9uZGVuIGFsIGRvY3VtZW50byBkZXNjcml0byBhbnRlcmlvcm1lbnRlLCBjb24gY2Fyw6FjdGVyIG5vIGV4Y2x1c2l2byB5IGRlIG1hbmVyYSBww7pibGljYSBlbiBhY2Nlc28gYWJpZXJ0byBhIHRyYXbDqXMgZGUgSW50ZXJuZXQsIHBhcmEgbG8gcXVlIGxhIEJpYmxpb3RlY2EgcHJvY2VkZXLDoSBhIGFyY2hpdmFybG9zIGVuIGVsIFJlcG9zaXRvcmlvIEluc3RpdHVjaW9uYWwuIEFzaW1pc21vIGF1dG9yaXphIGEgbGEgVW5pdmVyc2lkYWQgZGUgQnVyZ29zIGEgcmVhbGl6YXIgbGFzIHRyYW5zZm9ybWFjaW9uZXMgbmVjZXNhcmlhcyBkZSBmb3JtYXRvLCBubyBkZSBjb250ZW5pZG8sIHBhcmEgZ2FyYW50aXphciBsYSBwcmVzZXJ2YWNpw7NuIHkgZWwgYWNjZXNvIGVuIGVsIGZ1dHVyby4NCg0KRWwgYXV0b3IgZGlzcG9uZSwgZW4gdG9kbyBjYXNvLCBkZWwgZGVyZWNobyBhIHJldm9jYXIgZXN0YSBhdXRvcml6YWNpw7NuLg0KDQpMYSBjZXNpw7NuIGRlIGRlcmVjaG9zIGRlIGVzdGEgb2JyYSBzZSBlbmN1ZW50cmEgc3VqZXRhIGEgbGEgbGVnaXNsYWNpw7NuIHZpZ2VudGUgc29icmUgcHJvcGllZGFkIGludGVsZWN0dWFsIHkgZGVyZWNob3MgZGUgYXV0b3Iu