Decision templates for the classification of bioacoustic time series

Abstract Time series classification based on decision templates is the topic of this paper. The decision templates are built over several local feature vectors which are extracted from local time windows of the time series. To learn characteristic classifier outputs a set of decision templates is determined for each class. In the classification phase class memberships based on the decision templates and the classifier outputs are calculated. These class memberships are combined to compute the final classification result. We present algorithms to calculate multiple decision templates per class, and demonstrate their behaviour on two data sets, a synthetic data set, and a data set derived from a real world pattern recognition problem, the classification of bioacoustic time series. The fuzzy- k -nearest neighbour classifier is utilized as base classifier for both data sets.

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