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

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

    Research output: Contribution to journalArticleResearchpeer-review

    21 Citations (Scopus)


    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.

    Original languageEnglish
    Pages (from-to)493-528
    Number of pages36
    JournalKnowledge and Information Systems
    Issue number2
    Publication statusPublished - May 2018


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

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