Fusion of Similarity Measures for Time Series Classification
Identifieur interne : 002E10 ( Main/Merge ); précédent : 002E09; suivant : 002E11Fusion of Similarity Measures for Time Series Classification
Auteurs : Krisztian Buza [Allemagne] ; Alexandros Nanopoulos [Allemagne] ; Lars Schmidt-Thieme [Allemagne]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2011.
Abstract
Abstract: Time series classification, due to its applications in various domains, is one of the most important data-driven decision tasks of artificial intelligence. Recent results show that the simple nearest neighbor method with an appropriate distance measure performs surprisingly well, outperforming many state-of-the art methods. This suggests that the choice of distance measure is crucial for time series classification. In this paper we shortly review the most important distance measures of the literature, and, as major contribution, we propose a framework that allows fusion of these different similarity measures in a principled way. Within this framework, we develop a hybrid similarity measure. We evaluate it in context of time series classification on a large, publicly available collection of 35 real-world datasets and we show that our method achieves significant improvements in terms of classification accuracy.
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DOI: 10.1007/978-3-642-21222-2_31
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