Comparison of Measures for Characterizing the Difficulty of Time Series Classification
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-68323-7_19
Reference9 articles.
1. Bagnall, A.J., Lines, J., Bostrom, A., Large, J., Keogh, E.J.: The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances. Data Min. Knowl. Discov. 31(3), 606–660 (2017)
2. Christ, M., Braun, N., Neuffer, J., Kempa-Liehr, A.W.: Time series feature extraction on basis of scalable hypothesis tests (tsfresh - a python package). Neurocomputing 307, 72–77 (2018)
3. Dau, H.A., et al., Hexagon-ML: the UCR time series classification archive (2018)
4. Gower, J.C.: A general coefficient of similarity and some of its properties. Biometrics 27(4), 857 (1971)
5. Ho, T.K., Basu, M.: Complexity measures of supervised classification problems. IEEE Trans. Pattern Anal. Mach. Intell. 24(3), 289–300 (2002)
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