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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 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. eng http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess Attribution-NonCommercial-NoDerivatives 4.0 International Combining univariate approaches for ensemble change detection in multivariate data info:eu-repo/semantics/article