Skip to main content
SpringerLink
Log in

Early Random Shapelet Forest

  • Conference paper
  • First Online:
Discovery Science (DS 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9956))

Included in the following conference series:

Abstract

Early classification of time series has emerged as an increasingly important and challenging problem within signal processing, especially in domains where timely decisions are critical, such as medical diagnosis in health-care. Shapelets, i.e., discriminative sub-sequences, have been proposed for time series classification as a means to capture local and phase independent information. Recently, forests of randomized shapelet trees have been shown to produce state-of-the-art predictive performance at a low computational cost. In this work, they are extended to allow for early classification of time series. An extensive empirical investigation is presented, showing that the proposed algorithm is superior to alternative state-of-the-art approaches, in case predictive performance is considered to be more important than earliness. The algorithm allows for tuning the trade-off between accuracy and earliness, thereby supporting the generation of early classifiers that can be dynamically adapted to specific needs at low computational cost.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    In this study the information gain [17, 27] is used.

References

  1. Berndt, D.J., Clifford, J.: Using dynamic time warping to find patterns in time series. In: KDD Workshop, vol. 10, pp. 359–370. Seattle, WA (1994)

    Google Scholar 

  2. Breiman, L.: Bagging predictors. Mach. Learn. 24(2), 123–140 (1996)

    MathSciNet  MATH  Google Scholar 

  3. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  4. Dachraoui, A., Bondu, A., Cornuéjols, A.: Evaluation protocol of early classifiers over multiple data sets. In: Loo, C.K., Yap, K.S., Wong, K.W., Teoh, A., Huang, K. (eds.) ICONIP 2014. LNCS, vol. 8835, pp. 548–555. Springer, Heidelberg (2014). doi:10.1007/978-3-319-12640-1_66

    Google Scholar 

  5. Dachraoui, A., Bondu, A., Cornuéjols, A.: Early classification of time series as a non myopic sequential decision making problem. In: Appice, A., Rodrigues, P.P., Costa, V.S., Soares, C., Gama, J., Jorge, A. (eds.) ECML PKDD 2015. LNCS (LNAI), vol. 9284, pp. 433–447. Springer, Heidelberg (2015). doi:10.1007/978-3-319-23528-8_27

    Chapter  Google Scholar 

  6. Demšar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)

    MathSciNet  MATH  Google Scholar 

  7. Ding, H., Trajcevski, G., Scheuermann, P., Wang, X., Keogh, E.: Querying and mining of time series data: experimental comparison of representations and distance measures. Proc. VLDB Endowment 1(2), 1542–1552 (2008)

    Article  Google Scholar 

  8. Ghalwash, M.F., Radosavljevic, V., Obradovic, Z.: Utilizing temporal patterns for estimating uncertainty in interpretable early decision making. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 402–411. ACM (2014)

    Google Scholar 

  9. Grabocka, J., Schilling, N., Wistuba, M., Schmidt-Thieme, L.: Learning time-series shapelets. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 392–401. ACM (2014)

    Google Scholar 

  10. Hills, J., Lines, J., Baranauskas, E., Mapp, J., Bagnall, A.: Classification of time series by shapelet transformation. Data Min. Know. Discovery 28(4), 851–881 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  11. Ishiguro, K., Sawada, H., Sakano, H.: Multi-class boosting for early classification of sequences. In: Proceedings of the British Machine Vision Conference, pp. 24.1–24.10. BMVA Press (2010). doi:10.5244/C.24.24

  12. Karlsson, I., Papapetrou, P., Boström, H.: Forests of randomized shapelet trees. In: Gammerman, A., Vovk, V., Papadopoulos, H. (eds.) SLDS 2015. LNCS (LNAI), vol. 9047, pp. 126–136. Springer, Heidelberg (2015). doi:10.1007/978-3-319-17091-6_8

    Chapter  Google Scholar 

  13. Keogh, E., Zhu, Q., Hu, B., Hao, Y., Xi, X., Wei, L., Ratanamahatana, C.A.: The UCR time series classification/clustering homepage (2015). www.cs.ucr.edu/~eamonn/time_series_data/

  14. Lines, J., Davis, L.M., Hills, J., Bagnall, A.: A shapelet transform for time series classification. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 289–297. ACM (2012)

    Google Scholar 

  15. Mori, U., Mendiburu, A., Keogh, E., Lozano, J.A.: Reliable early classification of time series based on discriminating the classes over time. Data Min. Knowl. Discovery, 1–31 (2016)

    Google Scholar 

  16. Mueen, A., Keogh, E., Young, N.: Logical-shapelets: an expressive primitive for time series classification. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1154–1162. ACM (2011)

    Google Scholar 

  17. Quinlan, J.R.: C4.5: Programs for Machine Learning. Elsevier (1993)

    Google Scholar 

  18. Ratanamahatana, C.A., Keogh, E.: Everything you know about dynamic time warping is wrong. In: 3rd Workshop on Mining Temporal and Sequential Data, pp. 22–25 (2004)

    Google Scholar 

  19. Rodrıguez, J.J., Alonso, C.J., Boström, H.: Boosting interval based literals. Intell. Data Anal. 5, 245–262 (2001)

    MATH  Google Scholar 

  20. Rokach, L.: Ensemble-based classifiers. Artif. Intell. Rev. 33(1–2), 1–39 (2010)

    Article  Google Scholar 

  21. Rokach, L., Maimon, O.: Top-down induction of decision trees classifiers-a survey. IEEE Trans. Syst. Man Cybern. Part C: Appl. Rev. 35(4), 476–487 (2005)

    Article  Google Scholar 

  22. Sakoe, H., Chiba, S.: Dynamic programming algorithm optimization for spoken word recognition. Trans. ASSP 26, 43–49 (1978)

    Article  MATH  Google Scholar 

  23. Xing, Z., Pei, J., Dong, G., Yu, P.S.: Mining Sequence Classifiers for Early Prediction, pp. 644–655 (2008)

    Google Scholar 

  24. Xing, Z., Pei, J., Philip, S.Y.: Early classification on time series. Knowl. Inf. Syst. 31(1), 105–127 (2012)

    Article  Google Scholar 

  25. Xing, Z., Pei, J., Philip, S.Y., Wang, K.: Extracting interpretable features for early classification on time series. In: SDM, vol. 11, pp. 247–258. SIAM (2011)

    Google Scholar 

  26. Xing, Z., Pei, J., Yu, P.S.: Early prediction on time series: a nearest neighbor approach. In: Proceedings of the 21st International Jont Conference on Artifical Intelligence, pp. 1297–1302. Morgan Kaufmann Publishers Inc. (2009)

    Google Scholar 

  27. Ye, L., Keogh, E.: Time series shapelets: a new primitive for data mining. In: Proceedings of the 15th ACM SIGKDD. ACM (2009)

    Google Scholar 

Download references

Acknowledgments

This work was partly supported by project High-Performance Data Mining for Drug Effect Detection at Stockholm University, funded by the Swedish Foundation for Strategic Research (IIS11-0053).

Author information

Authors and Affiliations

  1. Department of Computer and Systems Sciences, Stockholm University, Stockholm, Sweden

    Isak Karlsson, Panagiotis Papapetrou & Henrik Boström

Authors
  1. Isak Karlsson
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Panagiotis Papapetrou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Henrik Boström
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Isak Karlsson .

Editor information

Editors and Affiliations

  1. Campus Middelhe, M.G.103a, Universiteit Antwerpen Campus Middelhe, M.G.103a, Antwerp, Belgium

    Toon Calders

  2. UniversitĂ  degli Studi di Bari Aldo Moro, Bari, Italy

    Michelangelo Ceci

  3. Bari, Italy

    Donato Malerba

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Karlsson, I., Papapetrou, P., Boström, H. (2016). Early Random Shapelet Forest. In: Calders, T., Ceci, M., Malerba, D. (eds) Discovery Science. DS 2016. Lecture Notes in Computer Science(), vol 9956. Springer, Cham. https://doi.org/10.1007/978-3-319-46307-0_17

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-46307-0_17

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46306-3

  • Online ISBN: 978-3-319-46307-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation