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2020 | OriginalPaper | Chapter

Localized Random Shapelets

Authors : Mael Guillemé, Simon Malinowski, Romain Tavenard, Xavier Renard

Published in: Advanced Analytics and Learning on Temporal Data

Publisher: Springer International Publishing

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Abstract

Shapelet models have attracted a lot of attention from researchers in the time series community, due in particular to its good classification performance. However, such models only inform about the presence/absence of local temporal patterns. Structural information about the localization of these patterns is ignored. In addition, end-to-end learning shapelet models tend to generate meaningless shapelets, leading to poorly interpretable models. In this paper, we aim at designing an interpretable shapelet model that takes into account the localization of the shapelets in the time series. Time series are transformed into feature vectors composed of both a distance and a localization information. Then, we design a hierarchical feature selection process using regularization. This process can be tuned to select, for each shapelet, either only its distance information or both distance and localization information. It is hence possible for every selected shapelet to analyze whether only the presence or the presence and the localization contributed to the decision process improving interpretability of the decision. Experiments show that this feature selection process has competitive performance compared to state-of-the-art shapelet-based classifiers, while providing better interpretability.
Footnotes
1
Note that the term distance is an abuse of notation since d(TS) is not a distance, mathematically speaking.
 
Literature
1.
go back to reference Bagnall, A., Lines, J., Bostrom, A., Large, J., Keogh, E.: The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances. Data Min. Knowl. Disc. 31, 606–660 (2016) MathSciNetCrossRef Bagnall, A., Lines, J., Bostrom, A., Large, J., Keogh, E.: The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances. Data Min. Knowl. Disc. 31, 606–660 (2016) MathSciNetCrossRef
4.
go back to reference Glorot, X., Bordes, A., Bengio, Y.: Deep sparse rectifier neural networks. In: Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, pp. 315–323 (2011) Glorot, X., Bordes, A., Bengio, Y.: Deep sparse rectifier neural networks. In: Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, pp. 315–323 (2011)
5.
go back to reference Grabocka, J., Schilling, N., Wistuba, M., Schmidt-Thieme, L.: Learning time-series shapelets. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 392–401 (2014) Grabocka, J., Schilling, N., Wistuba, M., Schmidt-Thieme, L.: Learning time-series shapelets. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 392–401 (2014)
6.
go back to reference Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:​1502.​03167 (2015) Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:​1502.​03167 (2015)
7.
go back to reference Lines, J., Davis, L.M., Hills, J., Bagnall, A.: A shapelet transform for time series classification. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 289–297 (2012) Lines, J., Davis, L.M., Hills, J., Bagnall, A.: A shapelet transform for time series classification. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 289–297 (2012)
8.
go back to reference Rakthanmanon, T., Keogh, E.: Fast shapelets: a scalable algorithm for discovering time series shapelets, pp. 668–676, May 2013 Rakthanmanon, T., Keogh, E.: Fast shapelets: a scalable algorithm for discovering time series shapelets, pp. 668–676, May 2013
9.
go back to reference Renard, X., Rifqi, M., Erray, W., Detyniecki, M.: Random-shapelet: an algorithm for fast shapelet discovery. In: IEEE International Conference on Data Science and Advanced Analytics, pp. 1–10 (2015) Renard, X., Rifqi, M., Erray, W., Detyniecki, M.: Random-shapelet: an algorithm for fast shapelet discovery. In: IEEE International Conference on Data Science and Advanced Analytics, pp. 1–10 (2015)
10.
go back to reference Renard, X., Rifqi, M., Fricout, G., Detyniecki, M.: EAST representation: fast discriminant temporal patterns discovery from time series. In: ECML/PKDD Workshop on Advanced Analytics and Learning on Temporal Data (2016) Renard, X., Rifqi, M., Fricout, G., Detyniecki, M.: EAST representation: fast discriminant temporal patterns discovery from time series. In: ECML/PKDD Workshop on Advanced Analytics and Learning on Temporal Data (2016)
11.
go back to reference Scardapane, S., Comminiello, D., Hussain, A., Uncini, A.: Group sparse regularization for deep neural networks. Neurocomputing 241, 81–89 (2017) CrossRef Scardapane, S., Comminiello, D., Hussain, A., Uncini, A.: Group sparse regularization for deep neural networks. Neurocomputing 241, 81–89 (2017) CrossRef
12.
go back to reference Schäfer, P.: The BOSS is concerned with time series classification in the presence of noise. Data Min. Knowl. Disc. 29(6), 1505–1530 (2015) MathSciNetCrossRef Schäfer, P.: The BOSS is concerned with time series classification in the presence of noise. Data Min. Knowl. Disc. 29(6), 1505–1530 (2015) MathSciNetCrossRef
13.
go back to reference Simon, N., Friedman, J., Hastie, T., Tibshirani, R.: A sparse-group lasso. J. Comput. Graph. Stat. 22(2), 231–245 (2013) MathSciNetCrossRef Simon, N., Friedman, J., Hastie, T., Tibshirani, R.: A sparse-group lasso. J. Comput. Graph. Stat. 22(2), 231–245 (2013) MathSciNetCrossRef
16.
go back to reference Tibshirani, R.: Regression shrinkage and selection via the lasso. J. R. Stat. Soc. Ser. B (Methodol.) 58, 267–288 (1996) MathSciNetMATH Tibshirani, R.: Regression shrinkage and selection via the lasso. J. R. Stat. Soc. Ser. B (Methodol.) 58, 267–288 (1996) MathSciNetMATH
17.
go back to reference Tieleman, T., Hinton, G.: Lecture 6.5-rmsprop: divide the gradient by a running average of its recent magnitude. COURSERA: Neural Netw. Mach. Learn. 4(2), 26–31 (2012) Tieleman, T., Hinton, G.: Lecture 6.5-rmsprop: divide the gradient by a running average of its recent magnitude. COURSERA: Neural Netw. Mach. Learn. 4(2), 26–31 (2012)
19.
go back to reference Ye, L., Keogh, E.: Time series shapelets: a new primitive for data mining. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 947–956 (2009) Ye, L., Keogh, E.: Time series shapelets: a new primitive for data mining. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 947–956 (2009)
Metadata
Title
Localized Random Shapelets
Authors
Mael Guillemé
Simon Malinowski
Romain Tavenard
Xavier Renard
Copyright Year
2020
DOI
https://doi.org/10.1007/978-3-030-39098-3_7

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