Abstract
The Elastic-Ensemble [7] has one of the longest build times of all constituents of the current state of the art algorithm for time series classification: the Hierarchical Vote Collective of Transformation-based Ensembles (HIVE-COTE) [8]. We investigate two simple and intuitive techniques to reduce the time spent training the Elastic Ensemble to consequently reduce HIVE-COTE train time. Our techniques reduce the effort involved in tuning parameters of each constituent nearest-neighbour classifier of the Elastic Ensemble. Firstly, we decrease the parameter space of each constituent to reduce tuning effort. Secondly, we limit the number of training series in each nearest neighbour classifier to reduce parameter option evaluation times during tuning. Experimentation over 10-folds of the UEA/UCR time-series classification problems show both techniques and give much faster build times and, crucially, the combination of both techniques give even greater speedup, all without significant loss in accuracy.
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References
Chen, L., Ng, R.: On the marriage of Lp-norms and edit distance. In: Proceedings 30th International Conference on Very Large Databases (VLDB) (2004)
Chen, Y., et al.: The UEA-UCR time series classification archive (2015). http://www.cs.ucr.edu/~eamonn/time_series_data/
Deng, H., Runger, G., Tuv, E., Vladimir, M.: A time series forest for classification and feature extraction. Inf. Sci. 239, 142–153 (2013)
Hills, J., Lines, J., Baranauskas, E., Mapp, J., Bagnall, A.: Classification of time series by shapelet transformation. Data Min. Knowl. Disc. 28(4), 851–881 (2014)
Jeong, Y., Jeong, M., Omitaomu, O.: Weighted dynamic time warping for time series classification. Pattern Recogn. 44, 2231–2240 (2011)
Keogh, E., Pazzani, M.: Derivative dynamic time warping. In: Proceedings 1st SIAM International Conference on Data Mining (SDM) (2001)
Lines, J., Bagnall, A.: Time series classification with ensembles of elastic distance measures. Data Min. Knowl. Disc. 29, 565–592 (2015)
Lines, J., Taylor, S., Bagnall, A.: Time series classification with HIVE-COTE: the hierarchical vote collective of transformation-based ensembles. ACM Trans. Knowl. Discovery From Data 12(5) (2018)
Marteau, P.: Time warp edit distance with stiffness adjustment for time series matching. IEEE Trans. Pattern Anal. Mach. Intell. 31(2), 306–318 (2009)
Schäfer, P.: The BOSS is concerned with time series classification in the presence of noise. Data Min. Knowl. Disc. 29(6), 1505–1530 (2015)
Schäfer, P., Leser, U.: Fast and accurate time series classification with weasel. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 637–646. ACM (2017)
Stefan, A., Athitsos, V., Das, G.: The Move-Split-Merge metric for time series. IEEE Trans. Knowl. Data Eng. 25(6), 1425–1438 (2013)
Ye, L., Keogh, E.: Time series shapelets: a novel technique that allows accurate, interpretable and fast classification. Data Min. Knowl. Disc. 22(1–2), 149–182 (2011)
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Oastler, G., Lines, J. (2019). A Significantly Faster Elastic-Ensemble for Time-Series Classification. In: Yin, H., Camacho, D., Tino, P., Tallón-Ballesteros, A., Menezes, R., Allmendinger, R. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2019. IDEAL 2019. Lecture Notes in Computer Science(), vol 11871. Springer, Cham. https://doi.org/10.1007/978-3-030-33607-3_48
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DOI: https://doi.org/10.1007/978-3-030-33607-3_48
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