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Integration of Reusable Systems pp 171–192Cite as

A Minimum Description Length Technique for Semi-Supervised Time Series Classification

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 263))

Abstract

In recent years the plunging costs of sensors/storage have made it possible to obtain vast amounts of medical telemetry, both in clinical settings and more recently, even in patient’s own homes . However for this data to be useful, it must be annotated. This annotation, requiring the attention of medical experts is very expensive and time consuming, and remains the critical bottleneck in medical analysis. The technique of Semi-supervised learning is the obvious way to reduce the need for human labor, however, most such algorithms are designed for intrinsically discrete objects such as graphs or strings, and do not work well in this domain, which requires the ability to deal with real-valued objects arriving in a streaming fashion. In this work we make two contributions. First, we demonstrate that in many cases a surprisingly small set of human annotated examples are sufficient to perform accurate classification. Second, we devise a novel parameter-free stopping criterion for semi-supervised learning. We evaluate our work with a comprehensive set of experiments on diverse medical data sources including electrocardiograms. Our experimental results suggest that our approach can typically construct accurate classifiers even if given only a single annotated instance.

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Acknowledgments

This research was funded by NSF grant IIS—1161997.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Kasetsart University, University of California, Riverside, CA, USA

    Nurjahan Begum, Bing Hu, Thanawin Rakthanmanon & Eamonn Keogh

Authors
  1. Nurjahan Begum
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  2. Bing Hu
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  3. Thanawin Rakthanmanon
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  4. Eamonn Keogh
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Corresponding authors

Correspondence to Nurjahan Begum or Bing Hu .

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Editors and Affiliations

  1. Laboratoire de Communication dans les Systèmes Informatiques, Ecole Nationale Supérieure d’Informatique, Algiers, Algeria

    Thouraya Bouabana-Tebibel

  2. SPAWAR Systems Center Pacific, San Diego, USA

    Stuart H. Rubin

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Begum, N., Hu, B., Rakthanmanon, T., Keogh, E. (2014). A Minimum Description Length Technique for Semi-Supervised Time Series Classification. In: Bouabana-Tebibel, T., Rubin, S. (eds) Integration of Reusable Systems. Advances in Intelligent Systems and Computing, vol 263. Springer, Cham. https://doi.org/10.1007/978-3-319-04717-1_8

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  • DOI: https://doi.org/10.1007/978-3-319-04717-1_8

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