Advertisement

Signal/Background Classification of Time Series for Biological Virus Detection

  • Dominic SiedhoffEmail author
  • Hendrik Fichtenberger
  • Pascal Libuschewski
  • Frank Weichert
  • Christian Sohler
  • Heinrich Müller
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8753)

Abstract

This work proposes translation-invariant features based on a wavelet transform that are used to classify time series as containing either relevant signals or noisy background. Due to the translation-invariant property, signals appearing at arbitrary locations in time have similar representations in feature space. Classification is carried out by a condensed \(k\)-Nearest-Neighbors classifier trained on these features, i.e. the training set is reduced for faster classification. This reduction is conducted by a \(k\)-means clustering of the original training set and using the obtained cluster centers as a new training set. The coreset-technique BICO is employed to accelerate this initial clustering for big datasets. The resulting feature extraction and classification pipeline is applied successfully in the context of biological virus detection. Data from Plasmon Assisted Microscopy of Nano-size Objects (PAMONO) is classified, achieving accuracy \(0.999\) for the most important classification task.

Keywords

Cluster Center Discrete Wavelet Transform Feature Ranking Time Series Classification Circular Shift 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

Part of the work on this paper has been supported by Deutsche Forschungsgemeinschaft (DFG) within the Collaborative Research Center SFB 876. URL: http://sfb876.tu-dortmund.de/

References

  1. 1.
    Alpaydin, E.: Voting over multiple condensed nearest neighbors. Artif. Intell. Rev. 11(1–5), 115–132 (1997)CrossRefGoogle Scholar
  2. 2.
    Angiulli, F.: Fast condensed nearest neighbor rule. In: Proceedings of the 22nd International Conference on Machine Learning, pp. 25–32 (2005)Google Scholar
  3. 3.
    Arthur, D., Vassilvitskii, S.: k-means++: the advantages of careful seeding. In: Proceedings of the 18th Symposium on Discrete Algorithms (SODA) (2007)Google Scholar
  4. 4.
    Coifman, R.R., Donoho, D.L.: Translation-invariant de-noising. In: Antoniadis, A., Oppenheim, G. (eds.) Wavelets and Statistics. Lecture Notes in Statistics, vol. 103. Springer, New York (1995)CrossRefGoogle Scholar
  5. 5.
    Dougherty, E.R.: An Introduction to Morphological Image Processing. SPIE Press, Bellingham (1992)Google Scholar
  6. 6.
    Fichtenberger, H., Gillé, M., Schmidt, M., Schwiegelshohn, C., Sohler, C.: BICO: BIRCH meets coresets for k-means clustering. In: Proceedings of the 21st European Symposium on Algorithms (ESA) (2013)Google Scholar
  7. 7.
    Gowda, K.C., Krishna, G.: The condensed nearest neighbor rule using the concept of mutual nearest neighborhood. IEEE Trans. Inf. Theory 25(4), 488–490 (1979)CrossRefGoogle Scholar
  8. 8.
    Har-Peled, S., Mazumdar, S.: On coresets for k-means and k-median clustering. In: Proceedings of the 36th Symposium on Theory of Computing (STOC), pp. 291–300 (2004)Google Scholar
  9. 9.
    Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Springer, New York (2009)CrossRefzbMATHGoogle Scholar
  10. 10.
    Kuncheva, L.I.: A stability index for feature selection. In: Artificial Intelligence and Applications (2007)Google Scholar
  11. 11.
    Li, D., Luo, H., Shi, Z.: Redundant DWT based translation invariant wavelet feature extraction for face recognition. In: ICPR (2008)Google Scholar
  12. 12.
    Libuschewski, P., Siedhoff, D., Timm, C., Gelenberg, A., Weichert, F.: Fuzzy-enhanced, real-time capable detection of biological viruses using a portable biosensor. In: Proceedings of the International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSIGNALS) (2013)Google Scholar
  13. 13.
    Lloyd, S.: Least squares quantization in PCM. IEEE Trans. Inf. Theory 28(2), 129–137 (1982)MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Ma, K., Tang, X.: Translation-invariant face feature estimation using discrete wavelet transform. In: Tang, Y.T., Wickershauser, V., Yuen, P.C., Li, C.-H. (eds.) WAA 2001. LNCS, vol. 2251, pp. 200–210. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  15. 15.
    Siedhoff, D., Fichtenberger, H., Libuschewski, P., Weichert, F., Sohler, C., Müller, H.: Signal/background classification of time series for biological virus detection - supplemental material. In: Jiang, X., Hornegger, J., Koch, R. (eds.) GCPR 2014. LNCS, vol. 8753, pp. 384–394. Springer, Heidelberg (2014)Google Scholar
  16. 16.
    Siedhoff, D., Libuschewski, P., Weichert, F., Zybin, A., Marwedel, P., Müller, H.: Modellierung und Optimierung eines Biosensors zur Detektion viraler Strukturen. In: Deserno, T.M., et al. (Hrsg.) Bildverarbeitung für die Medizin, pp. 108–113. Springer, Heidelberg (2014)Google Scholar
  17. 17.
    Yong, Z., Youwen, L., Shixiong, X.: An improved KNN text classification algorithm based on clustering. J. Comput. 4(3), 230–237 (2009)Google Scholar
  18. 18.
    Zybin, A., Kuritsyn, Y.A., Gurevich, E.L., Temchura, V.V., Ueberla, K., Niemax, K.: Real-time detection of single immobilized nanoparticles by surface plasmon resonance imaging. Plasmonics 5, 31–35 (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Dominic Siedhoff
    • 1
    Email author
  • Hendrik Fichtenberger
    • 2
  • Pascal Libuschewski
    • 3
  • Frank Weichert
    • 1
  • Christian Sohler
    • 2
  • Heinrich Müller
    • 1
  1. 1.Computer Science VII – Computer GraphicsTU DortmundDortmundGermany
  2. 2.Computer Science II – Efficient Algorithms and Complexity TheoryTU DortmundDortmundGermany
  3. 3.Computer Science XII – Embedded SystemsTU DortmundDortmundGermany

Personalised recommendations