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A Multi-measure Nearest Neighbor Algorithm for Time Series Classification

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 5290)

Abstract

In this paper, we have evaluated some techniques for the time series classification problem. Many distance measures have been proposed as an alternative to the Euclidean Distance in the Nearest Neighbor Classifier. To verify the assumption that the combination of various similarity measures may produce a more accurate classifier, we have proposed an algorithm to combine several measures based on weights. We have carried out a set of experiments to verify the hypothesis that the new algorithm is better than the classical ones. Our results show an improvement over the well-established Nearest-Neighbor with DTW (Dynamic Time Warping), but in general, they were obtained combining few measures in each problem used in the experimental evaluation.

Keywords

  • Data Mining
  • Machine Learning
  • Time Series Classification
  • Multi-Measure Classifier

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Fabris, F., Drago, I., Varejão, F.M. (2008). A Multi-measure Nearest Neighbor Algorithm for Time Series Classification. In: Geffner, H., Prada, R., Machado Alexandre, I., David, N. (eds) Advances in Artificial Intelligence – IBERAMIA 2008. IBERAMIA 2008. Lecture Notes in Computer Science(), vol 5290. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88309-8_16

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  • DOI: https://doi.org/10.1007/978-3-540-88309-8_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-88308-1

  • Online ISBN: 978-3-540-88309-8

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