Abstract
The classification of time series based on local features is discussed in this paper. In this context we discuss the topics data fusion, decision fusion, and temporal fusion. Three different classifier architectures for these fusion tasks are proposed. Some local features are automatically derived form the time and frequency domain and categorized through a fuzzy-k-nearest-neighbour rule. Soft decisions are combined to a crisp decision of the whole time series. Numerical results for all architectures are given for a data set (songs of crickets, recorded in Thailand and Ecuador) containing 35 different categories.
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References
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© 2001 Springer-Verlag Berlin Heidelberg
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Dietrich, C., Schwenker, F., Palm, G. (2001). Fusion Architectures for the Classification of Time Series. In: Dorffner, G., Bischof, H., Hornik, K. (eds) Artificial Neural Networks — ICANN 2001. ICANN 2001. Lecture Notes in Computer Science, vol 2130. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44668-0_105
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DOI: https://doi.org/10.1007/3-540-44668-0_105
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