Combining univariate approaches for ensemble change detection in multivariate data

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Combining univariate approaches for ensemble change detection in multivariate data. / Faithfull, William; Rodriguez, Juan; Kuncheva, Ludmila.
In: Information Fusion, Vol. 45, 01.01.2019, p. 202-214.

Research output: Contribution to journalArticlepeer-review

HarvardHarvard

Faithfull, W, Rodriguez, J & Kuncheva, L 2019, 'Combining univariate approaches for ensemble change detection in multivariate data', Information Fusion, vol. 45, pp. 202-214.

APA

Faithfull, W., Rodriguez, J., & Kuncheva, L. (2019). Combining univariate approaches for ensemble change detection in multivariate data. Information Fusion, 45, 202-214.

CBE

Faithfull W, Rodriguez J, Kuncheva L. 2019. Combining univariate approaches for ensemble change detection in multivariate data. Information Fusion. 45:202-214.

MLA

Faithfull, William, Juan Rodriguez, and Ludmila Kuncheva. "Combining univariate approaches for ensemble change detection in multivariate data". Information Fusion. 2019, 45. 202-214.

VancouverVancouver

Faithfull W, Rodriguez J, Kuncheva L. Combining univariate approaches for ensemble change detection in multivariate data. Information Fusion. 2019 Jan 1;45:202-214. Epub 2018 Feb 13.

Author

Faithfull, William ; Rodriguez, Juan ; Kuncheva, Ludmila. / Combining univariate approaches for ensemble change detection in multivariate data. In: Information Fusion. 2019 ; Vol. 45. pp. 202-214.

RIS

TY - JOUR

T1 - Combining univariate approaches for ensemble change detection in multivariate data

AU - Faithfull, William

AU - Rodriguez, Juan

AU - Kuncheva, Ludmila

PY - 2019/1/1

Y1 - 2019/1/1

N2 - 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.

AB - 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.

M3 - Article

VL - 45

SP - 202

EP - 214

JO - Information Fusion

JF - Information Fusion

SN - 1566-2535

ER -