SLDS 2015: Statistical Learning and Data Sciences pp 126-136 | Cite as
Forests of Randomized Shapelet Trees
Abstract
Shapelets have recently been proposed for data series classification, due to their ability to capture phase independent and local information. Decision trees based on shapelets have been shown to provide not only interpretable models, but also, in many cases, state-of-the-art predictive performance. Shapelet discovery is, however, computationally costly, and although several techniques for speeding up this task have been proposed, the computational cost is still in many cases prohibitive. In this work, an ensemble-based method, referred to as Random Shapelet Forest (RSF), is proposed, which builds on the success of the random forest algorithm, and which is shown to have a lower computational complexity than the original shapelet tree learning algorithm. An extensive empirical investigation shows that the algorithm provides competitive predictive performance and that a proposed way of calculating importance scores can be used to successfully identify influential regions.
Keywords
Data series classification Shapelets Decision trees EnsemblePreview
Unable to display preview. Download preview PDF.
References
- 1.Bagnall, A., Davis, L.M., Hills, J., Lines, J.: Transformation basedensembles for time series classification. In: SDM, vol.12, pp. 307–318. SIAM (2012)Google Scholar
- 2.Batista, G.E., Wang, X., Keogh, E.J.: A complexity-invariant distance measure for time series. In: SDM, vol. 11, pp. 699–710. SIAM (2011)Google Scholar
- 3.Berndt, D.J., Clifford, J.: Using dynamic time warping to find patterns in time series. In: KDD workshop, vol. 10, pp. 359–370. Seattle, WA (1994)Google Scholar
- 4.Breiman, L.: Bagging predictors. Machine Learning 24(2), 123–140 (1996)MATHMathSciNetGoogle Scholar
- 5.Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001)CrossRefMATHGoogle Scholar
- 6.Breiman, L., Friedman, J., Stone, C.J., Olshen, R.A.: Classification and regression trees. CRC Press (1984)Google Scholar
- 7.Cortes, C., Vapnik, V.: Support-vector networks. Machine Learning 20(3)Google Scholar
- 8.Demšar, J.: Statistical comparisons of classifiers over multiple data sets. The Journal of Machine Learning Research 7, 1–30 (2006)MATHGoogle Scholar
- 9.Deng, H., Runger, G., Tuv, E., Vladimir, M.: A time series forest for classification and feature extraction. Information Sciences 239, 142–153 (2013)CrossRefMathSciNetGoogle Scholar
- 10.Ding, H., Trajcevski, G., Scheuermann, P., Wang, X., Keogh, E.: Querying and mining of time series data: experimental comparison of representations and distance measures. Proc. of the VLDB Endowment 1(2), 1542–1552 (2008)CrossRefGoogle Scholar
- 11.Fayyad, U.M., Irani, K.B.: On the handling of continuous-valued attributes in decision tree generation. Machine Learning 8(1), 87–102 (1992)MATHGoogle Scholar
- 12.Gordon, D., Hendler, D., Rokach, L.: Fast randomized model generation for shapelet-based time series classification. arXiv preprint arXiv:1209.5038 (2012)
- 13.Hills, J., Lines, J., Baranauskas, E., Mapp, J., Bagnall, A.: Classification of time series by shapelet transformation. Data Mining and Know. Discovery 28(4) (2014)Google Scholar
- 14.Ho, T.K.: The random subspace method for constructing decision forests. IEEE Trans. on Pat. Analysis and Machine Intelligence 20(8), 832–844 (1998)CrossRefGoogle Scholar
- 15.Kampouraki, A., Manis, G., Nikou, C.: Heartbeat time series classification with support vector machines. Inf. Tech. in Biomedicine 13(4) (2009)Google Scholar
- 16.Keogh, E., Zhu, Q., Hu, B., Hao, Y., Xi, X., Wei, L., Ratanamahatana, C.A.: The ucr time series classification/clustering homepage, www.cs.ucr.edu/ eamonn/time_series_data/
- 17.Mueen, A., Keogh, E., Young, N.: Logical-shapelets: an expressive primitive for time series classification. In: Proc. 17th ACM SIGKDD. ACM (2011)Google Scholar
- 18.Rakthanmanon, T., Campana, B., Mueen, A., Batista, G., Westover, B., Zhu, Q., Zakaria, J., Keogh, E.: Searching and mining trillions of time series subsequences under dynamic time warping. In: Proc. of the 18th ACM SIGKDD. ACM (2012)Google Scholar
- 19.Rakthanmanon, T., Keogh, E.: Fast shapelets: a scalable algorithm for discovering time series shapelets. In: Proc. 13th SDM. SIAM (2013)Google Scholar
- 20.Rebbapragada, U., Protopapas, P., Brodley, C.E., Alcock, C.: Finding anomalous periodic time series. Machine Learning 74(3), 281–313 (2009)CrossRefGoogle Scholar
- 21.Sakoe, H., Chiba, S.. In: Transactions on ASSP, vol. 26, pp. 43–49Google Scholar
- 22.Schmidhuber, J.: Deep learning in neural networks: An overview. arXiv preprint arXiv:1404.7828 (2014)
- 23.Wang, X., Mueen, A., Ding, H., Trajcevski, G., Scheuermann, P., Keogh, E.: Experimental comparison of representation methods and distance measures for time series data. Data Mining and Knowl. Discovery 26(2) (2013)Google Scholar
- 24.Ye, L., Keogh, E.: Time series shapelets: a new primitive for data mining. In: Proc. of the 15th ACM SIGKDD. ACM (2009)Google Scholar