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.
A multi-measure nearest neighbor algorithm for time series classification / Fabris, Fábio; Drago, Idilio; Varejão, Flávio M.. - ELETTRONICO. - 5290:(2008), pp. 153-162. (Intervento presentato al convegno 11th Ibero-American Conference on Artificial Intelligence, IBERAMIA 2008 tenutosi a Lisbon, Portugal nel 2008) [10.1007/978-3-540-88309-8_16].
A multi-measure nearest neighbor algorithm for time series classification
DRAGO, IDILIO;
2008
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.File | Dimensione | Formato | |
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https://hdl.handle.net/11583/2659199
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