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Dissimilarity-based representations for one-class classification on time series
Pattern Recognition ( IF 7.196 ) Pub Date : 2019-11-20 , DOI: 10.1016/j.patcog.2019.107122
Stefano Mauceri; James Sweeney; James McDermott

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.

更新日期:2020-01-04

 

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