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


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.

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We thank anonymous reviewers for their very useful comments and suggestions. This work was supported in part by “Proyecto de Investigación de Excelencia de la Junta de Andalucía, P12-TIC-2958,” “Proyecto de Investigación del Ministerio de Economía y Competitividad, TIN2013-47210-P” and TIN-2016-81113-R. This work was partly performed while M. González held a travel grant from the Asociación Iberoamericana de Postgrado (AUIP), supported by Junta de Andalucía, to undertake a research stay at University of Granada.

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González, M., Bergmeir, C., Triguero, I. et al. Self-labeling techniques for semi-supervised time series classification: an empirical study. Knowl Inf Syst 55, 493–528 (2018).

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  • Semi-supervised classification
  • Self-labeled
  • Time series classification
  • Semi-supervised learning
  • Self-training