Abstract
The growing interest in time-series classification can be attributed to the intensively increasing amount of temporal data collected by widespread sensors. Often, human experts may only review a small portion of all the available data. Therefore, the available labeled data may not be representative enough and semi-supervised techniques may be necessary. In order to construct accurate classifiers, semi-supervised techniques learn both from labeled and unlabeled data. In this paper, we introduce a novel semi-supervised time-series classifier based on constrained hierarchical clustering and dynamic time warping. We discuss our approach in the framework of graph theory and evaluate it on 44 publicly available real-world time-series datasets from various domains. Our results show that our approach substantially outperforms the state-of-the-art semi-supervised time-series classifier. The results are also justified by statistical significance tests.
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Marussy, K., Buza, K. (2013). SUCCESS: A New Approach for Semi-supervised Classification of Time-Series. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2013. Lecture Notes in Computer Science(), vol 7894. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38658-9_39
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DOI: https://doi.org/10.1007/978-3-642-38658-9_39
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