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

  • Nurjahan Begum
  • Bing Hu
  • Thanawin Rakthanmanon
  • Eamonn Keogh
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 263)


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.


MDL Semi-supervised learning Stopping criterion Time series 



This research was funded by NSF grant IIS—1161997.


  1. 1.
    Besemer, J., Lomsadze, A., Borodovsky, M.: GeneMarkS: a self-training method for prediction of gene starts in microbial genomes. Implications for finding sequence motifs in regulatory regions. Nucleic Acids Res. 29(12), 2607–2618 (2001)CrossRefGoogle Scholar
  2. 2.
    Blum, A., Mitchell, T.: Combining labeled and unlabeled data with co-training. In: Proceedings of the 11th ACM Annual Conference on Computational Learning Theory, pp. 92–100 (1998)Google Scholar
  3. 3.
    Bouchard, D., Badler, N.: Semantic segmentation of motion capture using Laban movement analysis. In: Intelligent Virtual Agents, pp. 37–44. Springer, Heidelberg (2007)Google Scholar
  4. 4.
    Chapelle, O., Schölkopf, B., Zien, A.: Semi-Supervised Learning, vol. 2. MIT press, Cambridge (2006)CrossRefGoogle Scholar
  5. 5.
    Chazal, P.D., O’Dwyer, M., Reilly, R.B.: Automatic classification of heartbeats using ECG morphology and heartbeat interval features. IEEE Trans. Biomed. Eng. 51, 1196–1206 (2004)CrossRefGoogle Scholar
  6. 6.
    Chen, Y., Hu, B., Keogh, E., Batista, G.E.: DTW-D: time series semi-supervised learning from a single example. In: The 19th ACM SIGKDD, pp. 383–391 (2013)Google Scholar
  7. 7.
    Demšar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)MATHMathSciNetGoogle Scholar
  8. 8.
    Druck, G., Pal, C., Zhu, X., McCallum, A.: Semi-supervised classification with hybrid generative/discriminative methods. In: The 13th ACM SIGKDD (2007)Google Scholar
  9. 9.
    Florea, F., Müller, H., Rogozan, A., Geissbuhler, A., Darmoni, S.: Medical image categorization with MedIC and MedGIFT. In: Medical Informatics Europe (MIE) (2006)Google Scholar
  10. 10.
    Geurts, P.: Pattern extraction for time series classification. In: Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery, pp. 115–127 (2001)Google Scholar
  11. 11.
    Goldberger, A.L., et al.: PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101(23), 215–220 (2000)CrossRefGoogle Scholar
  12. 12.
    Greenwald, S.D., Patil, R.S., Mark, R.G.: Improved detection and classification of arrhythmias in noise-corrupted electrocardiograms using contextual information. In: Proceedings of IEEE Conference on Computing in Cardiology (1990)Google Scholar
  13. 13.
    Greenwald, S.D.: The Development and Analysis of a Ventricular Fibrillation Detector. M.S. thesis, MIT Department of Electrical Engineering and Computer Science, Cambridge (1986)Google Scholar
  14. 14.
    Grünwald, P.: A Tutorial Introduction to the Minimum Description Length Principle. MIT Press, Cambridge (2005)Google Scholar
  15. 15.
    Herwig, M.: Google’s Total Library: Putting the World’s Books on the Web (2007)Google Scholar
  16. 16.
    Hills, J., Lines, J., Baranauskas, E., Mapp, J., Bagnall, A.: Classification of time series by shapelet transformation. Data Min. Knowl. Disc. 2, 1–31 (2013)Google Scholar
  17. 17.
    Hu, B., Rakthanmanon, T., Hao, Y., Evans, S., Lonardi, S., Keogh, E.: Discovering the intrinsic cardinality and dimensionality of time series using MDL. In: Proceedings of ICDM, pp. 1086–1091 (2011)Google Scholar
  18. 18.
    Jones, P.D., Hulme, M.: Calculating regional climatic time series for temperature and precipitation: methods and illustrations. Int. J. Climatol. 16(4), 361–377 (1996)CrossRefGoogle Scholar
  19. 19.
    Keogh, E., Zhu, Q., Hu, B., Hao, Y., Xi, X., Wei, L., Ratanamahatana, C.A.: The UCR Time Series Classification/Clustering.
  20. 20.
    Li, M., Vitanyi, P.: An Introduction to Kolmogorov Complexity and Its Applications, 2nd edn. Springer, New York (1997)Google Scholar
  21. 21.
    Maeireizo, B., Litman, D., Hwa, R.: Co-training for predicting emotions with spoken dialogue data. In: Proceedings of ACL (2004)Google Scholar
  22. 22.
    McClosky, D., Charniak, E., Johnson, M.: Effective self-training for parsing. In: Proceedings of the Main Conference on Human Language Technology and Conference of the North American Chapter of the Association of Computational Linguistics, pp. 152–159 (2006)Google Scholar
  23. 23.
    Nemenyi, P.B.: Distribution-free Multiple Comparisons. PhD Thesis, Princeton University (1963)Google Scholar
  24. 24.
    Nguyen, M.N., Li, X.L., Ng, S.K.: Positive unlabeled learning for time series classification. In: Proceedings of AAAI (2011)Google Scholar
  25. 25.
    Nguyen, M.N., Li, X.L., Ng, S.K.: Ensemble Based Positive Unlabeled Learning for Time Series Classification. Database Systems for Advanced Applications. Springer, Heidelberg (2012)Google Scholar
  26. 26.
    Ordonez, P., Oates, T., Lombardi, M.E., Hernandez, G., Holmes, K.W., Fackler, J., Lehmann, C.U.: Visualization of multivariate time-series data in a Neonatal ICU. IBM J. Res. Dev. 56(5), 7–1 (2012)Google Scholar
  27. 27.
    Patton, A.J.: Copula-based models for financial time series. In: Andersen, T.G., Davis, R.A., Kreiss, J-P., Mikosch, T. (eds.) Handbook of Financial Time Series, pp. 767–785. Springer, Heidelberg (2009)Google Scholar
  28. 28.
    Philipose, M.: Large-Scale Human Activity Recognition Using Ultra-Dense Sensing. The Bridge, vol. 35, issue 4. National Academy of Engineering, Winter (2005)Google Scholar
  29. 29.
    Radovanovic, M., Nanopoulos, A., Ivanovic, M.: Time-series classification in many intrinsic dimensions. In: Proceedings of SIAM SDM, pp. 677–688 (2010)Google Scholar
  30. 30.
    Rakthanmanon, T., Keogh, E., Lonardi, S., Evans, S.: Time series epenthesis: clustering time series streams requires ignoring some data. In: Proceedings of ICDM (2011)Google Scholar
  31. 31.
    Raptis, M., Wnuk, K., Soatto, S.: Flexible dictionaries for action classification. In: The 1st International Workshop on Machine Learning for Vision-based Motion Analysis (2008)Google Scholar
  32. 32.
    Ratanamahatana, C.A., Keogh, E.: Making time-series classification more accurate using learned constraints. In: Proceedings of SIAM SDM (2004)Google Scholar
  33. 33.
    Ratanamahatana, C.A., Wanichsan, D.: Stopping criterion selection for efficient semi-supervised time series classification. In: Lee, R.Y. (ed.) Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, Studies in Computational Intelligence, vol. 149, pp. 1–14. Springer (2008)Google Scholar
  34. 34.
    Rosenberg, C., Hebert, M., Schneiderman, H.: Semi-supervised Self-training of Object Detection Models. WACV/MOTION, 29–36 (2005)Google Scholar
  35. 35.
    Simon, B.P., Eswaran, C.: An ECG classifier designed using modified decision based neural networks. Comput. Biomed. Res. 30(4), 257–272 (1997)CrossRefGoogle Scholar
  36. 36.
    Sun, A., Grishman, R.: Semi-supervised semantic pattern discovery with guidance from unsupervised pattern clusters. In: Proceedings of the 23rd International Conference on Computational Linguistics: Posters, pp. 1194–1202 (2010)Google Scholar
  37. 37.
    Sykacek, P., Roberts, S.J.: Bayesian time series classification. In: Jordan, M., Reams, M., Solla, S. (eds.) Advances in Neural Information Processing Systems. MIT Press, Cambridge (2002)Google Scholar
  38. 38.
    Tsumoto, S.: Rule discovery in large time-series medical databases. In: In: Zytkow, J., Rauch, J. (eds.) Principles of Data Mining and Knowledge Discovery, pp. 23–31. Springer, Heidelberg (1999)Google Scholar
  39. 39.
    Veeraraghavan, A., Chellappa, R., Srinivasan, M.: Shape and behavior encoded tracking of bee dances. IEEE Trans. Pattern Anal. Mach. Intell. 30(3), 463–476 (2008)CrossRefGoogle Scholar
  40. 40.
    Wang, X., Mueen, A., Ding, H., Trajcevski, G., Scheuermann, P., Keogh, E.J.: Experimental comparison of representation methods and distance measures for time series data. Data Min. Knowl. Discov. 26(2), 275–309 (2013)CrossRefMathSciNetGoogle Scholar
  41. 41.
    Wei, L., Keogh, E.: Semi-supervised time series classification. In: Proceedings of SIGKDD (2006)Google Scholar
  42. 42.
    Xi, X., Keogh, E., Shelton, C., Wei, L., Ratanamahatana, C.A.: Fast time series classification using numerosity reduction. In: Proceedings of the 23rd ACM International Conference on Machine Learning, pp. 1033–1040 (2006)Google Scholar
  43. 43.
    Yarowsky, D.: Unsupervised word sense disambiguation rivaling supervised methods. In: Proceedings of ACL (1995)Google Scholar
  44. 44.
    Zhu, X.: Semi-supervised Learning Literature Survey. Technical Report No. 1530. Computer Sciences, University of Wisconsin-Madison (2005)Google Scholar
  45. 45.

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringKasetsart University, University of CaliforniaRiversideUSA

Personalised recommendations