2019
Faithfull, William J; Rodríguez, Juan José; Kuncheva, Ludmila I
Combining univariate approaches for ensemble change detection in multivariate data Journal Article
In: Information Fusion, vol. 45, pp. 202-214, 2019, ISSN: 1566-2535.
Abstract | Links | BibTeX | Tags: Change detection, Ensemble methods, Multivariate data, SELECTED
@article{Faithfull2019,
title = {Combining univariate approaches for ensemble change detection in multivariate data},
author = {William J Faithfull and Juan José Rodríguez and Ludmila I Kuncheva},
url = {https://www.sciencedirect.com/science/article/pii/S1566253517301239},
doi = {10.1016/j.inffus.2018.02.003},
issn = {1566-2535},
year = {2019},
date = {2019-01-01},
journal = {Information Fusion},
volume = {45},
pages = {202-214},
abstract = {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.},
keywords = {Change detection, Ensemble methods, Multivariate data, SELECTED},
pubstate = {published},
tppubtype = {article}
}
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.