Abstract
Classification of time series is an important task with many challenging applications like brain wave (EEG) analysis, signature verification or speech recognition. In this paper we show how characteristic local patterns (motifs) can improve the classification accuracy. We introduce a new motif class, generalized semi-continuous motifs. To allow flexibility and noise robustness, these motifs may include gaps of various lengths, generic and more specific wildcards. We propose an efficient algorithm for mining generalized sequential motifs. In experiments on real medical data, we show how generalized semi-continuous motifs improve the accuracy of SVMs and Bayesian Networks for time series classification.
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Buza, K., Schmidt-Thieme, L. (2009). Motif-Based Classification of Time Series with Bayesian Networks and SVMs. In: Fink, A., Lausen, B., Seidel, W., Ultsch, A. (eds) Advances in Data Analysis, Data Handling and Business Intelligence. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01044-6_9
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DOI: https://doi.org/10.1007/978-3-642-01044-6_9
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