Advertisement

Applying General-Purpose Data Reduction Techniques for Fast Time Series Classification

  • Stefanos Ougiaroglou
  • Leonidas Karamitopoulos
  • Christos Tatoglou
  • Georgios Evangelidis
  • Dimitris A. Dervos
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8131)

Abstract

The one-nearest neighbour classifier is a widely-used time series classification method. However, its efficiency depends on the size of the training set as well as on data dimensionality. Although many speed-up methods for fast time series classification have been proposed, state-of-the-art, non-parametric data reduction techniques have not been exploited on time series data. This paper presents an experimental study where known prototype selection and abstraction data reduction techniques are evaluated both on original data and a dimensionally reduced representation form of the same data from seven time series datasets. The results show that data reduction, even when applied on dimensionally reduced data, can in some cases improve the accuracy and at the same time reduce the computational cost of classification.

Keywords

time series classification nearest neighbor data reduction 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Aha, D.W.: Tolerating noisy, irrelevant and novel attributes in instance-based learning algorithms. Int. J. Man-Mach. Stud. 36(2), 267–287 (1992)CrossRefGoogle Scholar
  2. 2.
    Aha, D.W., Kibler, D., Albert, M.K.: Instance-based learning algorithms. Mach. Learn. 6(1), 37–66 (1991)Google Scholar
  3. 3.
    Buza, K., Nanopoulos, A., Schmidt-Thieme, L.: INSIGHT: Efficient and effective instance selection for time-series classification. In: Huang, J.Z., Cao, L., Srivastava, J. (eds.) PAKDD 2011, Part II. LNCS, vol. 6635, pp. 149–160. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  4. 4.
    Chen, C.H., Jóźwik, A.: A sample set condensation algorithm for the class sensitive artificial neural network. Pattern Recogn. Lett. 17, 819–823 (1996)CrossRefGoogle Scholar
  5. 5.
    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 Endow. 1(2), 1542–1552 (2008)Google Scholar
  6. 6.
    Garcia, S., Derrac, J., Cano, J., Herrera, F.: Prototype selection for nearest neighbor classification: Taxonomy and empirical study. IEEE Trans. Pattern Anal. Mach. Intell. 34(3), 417–435 (2012)CrossRefGoogle Scholar
  7. 7.
    Hart, P.E.: The condensed nearest neighbor rule. IEEE Transactions on Information Theory 14(3), 515–516 (1968)CrossRefGoogle Scholar
  8. 8.
    Keogh, E.J., Pazzani, M.J.: A simple dimensionality reduction technique for fast similarity search in large time series databases. In: Terano, T., Liu, H., Chen, A.L.P. (eds.) PAKDD 2000. LNCS, vol. 1805, pp. 122–133. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  9. 9.
    Ougiaroglou, S., Evangelidis, G.: Efficient dataset size reduction by finding homogeneous clusters. In: Proceedings of the Fifth Balkan Conference in Informatics, BCI 2012, pp. 168–173. ACM, New York (2012)CrossRefGoogle Scholar
  10. 10.
    Sánchez, J.S.: High training set size reduction by space partitioning and prototype abstraction. Pattern Recognition 37(7), 1561–1564 (2004)CrossRefGoogle Scholar
  11. 11.
    Triguero, I., Derrac, J., Francisco Herrera, S.G.: A taxonomy and experimental study on prototype generation for nearest neighbor classification. IEEE Transactions on Systems, Man, and Cybernetics, Part C 42(1), 86–100 (2012)CrossRefGoogle Scholar
  12. 12.
    Xi, X., Keogh, E., Shelton, C., Wei, L., Ratanamahatana, C.A.: Fast time series classification using numerosity reduction. In: Proceedings of the 23rd International Conference on Machine Learning, ICML 2006, pp. 1033–1040. ACM, New York (2006)Google Scholar
  13. 13.
    Yi, B.K., Faloutsos, C.: Fast time sequence indexing for arbitrary lp norms. In: Proceedings of the 26th International Conference on Very Large Data Bases, VLDB 2000, pp. 385–394. Morgan Kaufmann Publishers Inc., San Francisco (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Stefanos Ougiaroglou
    • 1
  • Leonidas Karamitopoulos
    • 2
  • Christos Tatoglou
    • 2
  • Georgios Evangelidis
    • 1
  • Dimitris A. Dervos
    • 2
  1. 1.Department of Applied InformaticsUniversity of MacedoniaGreece
  2. 2.Information Technology DepartmentAlexander TEI of ThessalonikiSindosGreece

Personalised recommendations