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An Iterative Feature Filter for Sensor Timeseries in Pervasive Computing Applications

  • Davide Bacciu
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 459)

Abstract

The paper discusses an efficient feature selection approach for multivariate timeseries of heterogeneous sensor data within a pervasive computing scenario. An iterative filtering procedure is devised to reduce information redundancy measured in terms of timeseries cross-correlation. The algorithm is capable of identifying non-redundant sensor sources in an unsupervised fashion even in presence of a large proportion of noisy features. A comparative experimental analysis on real-world data from pervasive computing applications is provided, showing that the algorithm addresses major limitations of unsupervised filters in literature when dealing with sensor timeseries.

Keywords

Feature Selection Wireless Sensor Network Feature Subset Pervasive Computing Multivariate Time Series 
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.

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References

  1. 1.
    Ye, J., Dobson, S., McKeever, S.: Review: Situation identification techniques in pervasive computing: A review. Pervasive Mob. Comput. 8(1), 36–66 (2012)CrossRefGoogle Scholar
  2. 2.
    Bacciu, D., Barsocchi, P., Chessa, S., Gallicchio, C., Micheli, A.: An experimental characterization of reservoir computing in ambient assisted living applications. Neural Computing and Applications, 1–14 (2013)Google Scholar
  3. 3.
    Jaeger, H., Haas, H.: Harnessing nonlinearity: Predicting chaotic systems and saving energy in wireless communication. Science 304(5667), 78–80 (2004)CrossRefGoogle Scholar
  4. 4.
    Guyon, I., Weston, J., Barnhill, S., Vapnik, V.: Gene selection for cancer classification using support vector machines. Mach. Learn. 46(1-3), 389–422 (2002)CrossRefzbMATHGoogle Scholar
  5. 5.
    Yang, K., Yoon, H., Shahabi, C.: A supervised feature subset selection technique for multivariate time series. In: Proc. of FSDM 2005, pp. 92–101 (2005)Google Scholar
  6. 6.
    Han, M., Liu, X.: Feature selection techniques with class separability for multivariate time series. Neurocomput. 110, 29–34 (2013)CrossRefGoogle Scholar
  7. 7.
    García-Pajares, R., Benítez, J.M., Sainz-Palmero, G.: Frasel: a consensus of feature ranking methods for time series modelling. Soft Computing 17(8), 1489–1510 (2013)CrossRefGoogle Scholar
  8. 8.
    Yoon, H., Yang, K., Shahabi, C.: Feature subset selection and feature ranking for multivariate time series. IEEE Trans. Knowl. Data Eng. 17(9), 1186–1198 (2005)CrossRefGoogle Scholar
  9. 9.
    Cheema, S., Henne, T., Koeckemann, U., Prassler, E.: Applicability of feature selection on multivariate time series data for robotic discovery. In: Proc. of ICACTE 2010., vol. 2, pp. 592–597 (2010)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Davide Bacciu
    • 1
  1. 1.Dipartimento di InformaticaUniversità di PisaItaly

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