Abstract
In this paper, we study the problem of classification of sequences of temporal intervals. Our main contribution is the STIFE framework for extracting relevant features from interval sequences to build feature-based classifiers. STIFE uses a combination of basic static metrics, shapelet discovery and selection, as well as distance-based approaches. Additionally, we propose an improved way of computing the state of the art IBSM distance measure between two interval sequences, that reduces both runtime and memory needs from pseudo-polynomial to fully polynomial, which greatly reduces the runtime of distance based classification approaches. Our empirical evaluation not only shows that STIFE provides a very fast classification time in all evaluated scenarios but also reveals that a random forests using STIFE achieves similar or better accuracy than the state of the art k-NN classifier.
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Notes
- 1.
Implementation available at: https://github.com/leonbornemann/stife.
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Bornemann, L., Lecerf, J., Papapetrou, P. (2016). STIFE: A Framework for Feature-Based Classification of Sequences of Temporal Intervals. In: Calders, T., Ceci, M., Malerba, D. (eds) Discovery Science. DS 2016. Lecture Notes in Computer Science(), vol 9956. Springer, Cham. https://doi.org/10.1007/978-3-319-46307-0_6
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DOI: https://doi.org/10.1007/978-3-319-46307-0_6
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