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- W135909296 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." @default.
- W135909296 created "2016-06-24" @default.
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- W135909296 date "2013-01-01" @default.
- W135909296 modified "2023-09-23" @default.
- W135909296 title "SUCCESS: A New Approach for Semi-supervised Classification of Time-Series" @default.
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- W135909296 doi "https://doi.org/10.1007/978-3-642-38658-9_39" @default.
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