2026-04-17T03:41:45Zhttps://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. . Rodríguez Diez, Juan José Kuncheva, Ludmila I. . 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