One-Class Time Series Classification

DC FieldValueLanguage
dc.contributor.authorMauceri, Stefano-
dc.date.accessioned2022-04-29T15:07:10Z-
dc.date.available2022-04-29T15:07:10Z-
dc.date.copyright2020 the Authoren_US
dc.date.issued2020-
dc.identifier.urihttp://hdl.handle.net/10197/12812-
dc.description.abstractThis thesis contributes to the state of the art of time series classification and machine learning by investigating three novel data-driven representations for time series in the context of one-class classification. The one-class assumption is useful for all classification problems where only data of a single class is available for training a classifier, or those where it is not known if novel classes may appear at prediction time or what they could look like. Notable examples that can benefit from our research are: anomaly or novelty detection, fault detection, identity authentication, etc. The common thread of our research is to represent time series as feature-vectors then used for classification. The features we extract are: (1) features constructed using dissimilarity measures; (2) features constructed using an evolutionary algorithm; (3) latent features constructed using neural networks. The proposed representations are thoroughly investigated in a variety of one-class classification experiments involving numerous benchmark methods, the 85 data-sets of the UCR/UEA archive and a data-set provided by ICON plc. The key difference between one-class classification and binary or multi-class classification is in the amount of effort needed to gather training data. Binary and multi-class classifiers require exhaustively labelled training data. This can be difficult for problems where all but the samples of one class are scarcely available and ill-defined, e.g. anomaly detection. Or again, gathering labelled data can simply be impossible due to the cost of expert labour required to construct an appropriate data-set. Conversely, one-class classifiers are trained using only samples from a single class. We present a subject authentication problem through accelerometer data as a case study that motivates our research on one-class time series classification. We argue that it is not realistic to assume we can gather labelled training data that represent well both the subject of interest and a fixed population of "others". Hence, the need to learn a classifier using data related to the subject of interest only. We demonstrate that, with respect to the use of raw time series, feature-based representations allow substantial and compelling savings in terms of storage and computational requirements, facilitate the interpretability of the solutions found, and enable visualisation of time series data-sets. We find that these advantages come at the cost of a slight loss in terms of classification performance with respect to a 1-nearest neighbour classifier on raw data. However, by examining data-sets one by one we detail how our representations can outperform raw time series. Furthermore, for some applications, e.g. embedded systems, storage and computational requirements may be more important than a slight loss in classification performance.en_US
dc.language.isoenen_US
dc.publisherUniversity College Dublin. School of Businessen_US
dc.subjectTime series classificationen_US
dc.subjectOne-class classificationen_US
dc.subjectAnomaly detectionen_US
dc.subjectRepresentation learningen_US
dc.titleOne-Class Time Series Classificationen_US
dc.typeDoctoral Thesisen_US
dc.statusPeer revieweden_US
dc.type.qualificationnamePh.D.en_US
dc.neeo.contributorMauceri|Stefano|aut|-
dc.date.updated2021-12-08en
dc.rights.licensehttps://creativecommons.org/licenses/by-nc-nd/3.0/ie/en_US
dc.contributor.orcid0000-0001-9795-5310en
dc.type.qualificationnamefreetextPhDen_US
item.grantfulltextopen-
item.fulltextWith Fulltext-
Appears in Collections:Business Theses
Files in This Item:
 File SizeFormat
Download103118471.pdf3.47 MBAdobe PDF
Show simple item record

Page view(s)

73
Last Week
5
Last month
26
checked on Jun 28, 2022

Download(s)

22
checked on Jun 28, 2022

Google ScholarTM

Check


If you are a publisher or author and have copyright concerns for any item, please email research.repository@ucd.ie and the item will be withdrawn immediately. The author or person responsible for depositing the article will be contacted within one business day.