Statistical hypothesis testing for chemical detection in changing environments

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

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)

6 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationIEEE SSCI 2014
Subtitle of host publication2014 IEEE Symposium Series on Computational Intelligence - CIDUE 2014: 2014 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments, Proceedings
PublisherIEEE Press
Pages77-84
Number of pages8
ISBN (Electronic)9781479945160
DOIs
Publication statusPublished - 12 Jan 2014
Event2014 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments, CIDUE 2014 - Orlando, United States of America
Duration: 09 Dec 201412 Dec 2014

Publication series

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

Conference

Conference2014 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments, CIDUE 2014
Country/TerritoryUnited States of America
CityOrlando
Period09 Dec 201412 Dec 2014

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