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Dissimilarity-based representations for one-class classification on time series

Mauceri, Stefano
; 
Sweeney, James
; 
McDermott, James
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Identifiers
https://hdl.handle.net/10379/18476
https://doi.org/10.13025/29270
Repository DOI
https://doi.org/10.1016/j.patcog.2019.107122
Publication Date
2019-11-25
Keywords
one-class classification
Type
journal article
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Citation
Mauceri, Stefano, Sweeney, James, & McDermott, James. (2020). Dissimilarity-based representations for one-class classification on time series. Pattern Recognition, 100, 107122. doi:https://doi.org/10.1016/j.patcog.2019.107122
Abstract
In several real-world classification problems it can be impractical to collect samples from classes other than the one of interest, hence the need for classifiers trained on a single class. There is a rich literature concerning binary and multi-class time series classification but less concerning one-class learning. In this study, we investigate the little-explored one-class time series classification problem. We represent time series as vectors of dissimilarities from a set of time series referred to as prototypes. Based on this approach, we evaluate a Cartesian product of 12 dissimilarity measures, and 8 prototype methods (strategies to select prototypes). Finally, a one-class nearest neighbor classifier is used on the dissimilarity-based representations (DBR). Experimental results show that DBR are competitive overall when compared with a strong baseline on the data-sets of the UCR/UEA archive. Additionally, DBR enable dimensionality reduction, and visual exploration of data-sets.
Funder
ICON plc.
Publisher
Elsevier
Publisher DOI
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International
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Collections
Computer Science (Scholarly Articles)