Self-labeling techniques for semi-supervised time series classification: an empirical study

González, Mabel; Bergmeir, Christoph; Triguero, Isaac; Rodríguez, Yanet; Benítez, José M.

Abstract

An increasing amount of unlabeled time series data available render the semi-supervised paradigm a suitable approach to tackle classification problems with a reduced quantity of labeled data. Self-labeled techniques stand out from semi-supervised classification methods due to their simplicity and the lack of strong assumptions about the distribution of the labeled and unlabeled data. This paper addresses the relevance of these techniques in the time series classification context by means of an empirical study that compares successful self-labeled methods in conjunction with various learning schemes and dissimilarity measures. Our experiments involve 35 time series datasets with different ratios of labeled data, aiming to measure the transductive and inductive classification capabilities of the self-labeled methods studied. The results show that the nearest-neighbor rule is a robust choice for the base classifier. In addition, the amending and multi-classifier self-labeled-based approaches reveal a promising attempt to perform semi-supervised classification in the time series context.

Citation

González, M., Bergmeir, C., Triguero, I., Rodríguez, Y., & Benítez, J. M. (in press). Self-labeling techniques for semi-supervised time series classification: an empirical study. Knowledge and Information Systems, https://doi.org/10.1007/s10115-017-1090-9

• Journal Article Type: Article
• Acceptance Date: Jul 28, 2017
• Online Publication Date: Aug 8, 2017
• Deposit Date: Aug 11, 2017
• Publicly Available Date: Aug 9, 2018
• Journal: Knowledge and Information Systems
• Print ISSN: 0219-1377
• Electronic ISSN: 0219-3116
• Publisher: Springer Verlag
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.1007/s10115-017-1090-9
• Keywords: Semi-supervised classification; Self-labeled; Time series classification; Semi-supervised learning; Self-training
• Public URL: http://eprints.nottingham.ac.uk/id/eprint/44845
• Publisher URL: https://link.springer.com/article/10.1007%2Fs10115-017-1090-9
• Copyright Statement: Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf
• Additional Information: The final publication is available at Springer via http://dx.doi.org/10.1007/s10115-017-1090-9