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

Stefano Mauceri, James Sweeney, James McDermott

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Article number107122
JournalPattern Recognition
Volume100
DOIs
Publication statusPublished - Apr 2020

Keywords

  • Dissimilarity-based representations
  • One-class classification
  • Time series

Fingerprint

Dive into the research topics of 'Dissimilarity-based representations for one-class classification on time series'. Together they form a unique fingerprint.

Cite this