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Title: Applying General-Purpose Data Reduction Techniques for Fast Time Series Classification
Authors: Ougiaroglou, Stefanos
Karamitopoulos, Leonidas
Tatoglou, Christos
Evangelidis, Georgios
Dervos, Dimitris A.
Type: Conference Paper
Subjects: FRASCATI::Natural sciences::Computer and information sciences
Keywords: time series classification
nearest neighbor
data reduction
Issue Date: 2013
Volume: 8131
First Page: 34
Last Page: 41
Volume Title: Artificial Neural Networks and Machine Learning – ICANN 2013
Part of Series: Lecture Notes in Computer Science
Part of Series: Lecture Notes in Computer Science
Abstract: The one-nearest neighbour classifier is a widely-used time series classification method. However, its efficiency depends on the size of the training set as well as on data dimensionality. Although many speed-up methods for fast time series classification have been proposed, state-of-the-art, non-parametric data reduction techniques have not been exploited on time series data. This paper presents an experimental study where known prototype selection and abstraction data reduction techniques are evaluated both on original data and a dimensionally reduced representation form of the same data from seven time series datasets. The results show that data reduction, even when applied on dimensionally reduced data, can in some cases improve the accuracy and at the same time reduce the computational cost of classification.
ISBN: 978-3-642-40727-7
ISSN: 0302-9743
Other Identifiers: 10.1007/978-3-642-40728-4_5
Appears in Collections:Department of Applied Informatics

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