Statistical hypothesis testing for chemical detection in changing environments

Anna Ladi, Jon Timmis, Andy M. Tyrrell, Peter J. Hickey

Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion CynhadleddTrafodion Cynhadledd (Nid-Cyfnodolyn fathau)

6 Dyfyniadau(SciVal)

Crynodeb

This paper addresses the problem of adaptive chemical detection, using the Receptor Density Algorithm (RDA), an immune inspired anomaly detection algorithm. Our approach is to first detect when and if something has changed in the environment and then adapt the RDA to this change. Statistical hypothesis testing is used to determine whether there has been concept drift in consecutive time windows of the data. Five different statistical methods are tested on mass spectrometry data, enhanced with artificial events that signify a changing environment. The results show that, while no one method is universally best, statistical hypothesis testing performs reasonably well on the context of chemical sensing and it can differentiate between anomalies and concept drift.

Iaith wreiddiolSaesneg
TeitlIEEE SSCI 2014
Is-deitl2014 IEEE Symposium Series on Computational Intelligence - CIDUE 2014: 2014 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments, Proceedings
CyhoeddwrIEEE Press
Tudalennau77-84
Nifer y tudalennau8
ISBN (Electronig)9781479945160
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsCyhoeddwyd - 12 Ion 2014
Digwyddiad2014 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments, CIDUE 2014 - Orlando, Unol Daleithiau America
Hyd: 09 Rhag 201412 Rhag 2014

Cyfres gyhoeddiadau

EnwIEEE SSCI 2014: 2014 IEEE Symposium Series on Computational Intelligence - CIDUE 2014: 2014 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments, Proceedings

Cynhadledd

Cynhadledd2014 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments, CIDUE 2014
Gwlad/TiriogaethUnol Daleithiau America
DinasOrlando
Cyfnod09 Rhag 201412 Rhag 2014

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