Abstract
Analyzing time-series is a task of rising interest in machine learning. At the same time developing interpretable machine learning tools is the recent challenge proposed by the industry to ease use of these tools by engineers and domain experts. In the paper we address the problem of generating interpretable classification of time-series data. We propose to extend the classical decision tree machine learning algorithm to Multi-operator Temporal Decision Trees (MTDT). The resulting algorithm provides interpretable decisions, thus improving the results readability, while preserving the classification accuracy. Aside MTDT we provide an interactive visualization tool allowing a user to analyse the data, their intrinsic regularities and the learned tree model.
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References
Ikats visualization tool. http://ama.liglab.fr/~software/ikats/demo/
Bagnall, A., Lines, J., Hills, J., Bostrom, A.: Time-series classification with COTE: the collective of transformation-based ensembles. In: 32nd IEEE, ICDE 2016, Helsinki, Finland, 16–20 May 2016, pp. 1548–1549 (2016)
Chen, Y., Keogh, E., Hu, B., Begum, N., Bagnall, A., Mueen, A., Batista, G.: The UCR time series classification archive, July 2015. www.cs.ucr.edu/~eamonn/time_series_data/
Douzal-Chouakria, D., Amblard, C.: Classification trees for time series. Pattern Recogn. 45, 1076–1091 (2012)
Esling, P., Agón, C.: Time-series data mining. ACM Comput. Surv. 12:1–12:34 (2012)
Fu, T.: A review on time series data mining. Eng. Appl. Artif. Intell. 24, 164–181 (2011)
Grabocka, J., Schilling, N., Wistuba, M., Schmidt-Thieme, L.: Learning time-series shapelets. In: The 20th ACM SIGKDD, KDD 2014, New York, NY, USA, 24–27 August 2014, pp. 392–401 (2014)
Kate, R.J.: Using dynamic time warping distances as features for improved time series classification. Data Min. Knowl. Discov. 30, 283–312 (2016)
Keogh, E.J., Kasetty, S.: On the need for time series data mining benchmarks: a survey and empirical demonstration. Data Min. Knowl. Discov. 7, 349–371 (2003)
Lin, J., Keogh, E.J., Wei, L., Lonardi, S.: Experiencing SAX: a novel symbolic representation of time series. Data Min. Knowl. Discov. 15, 107–144 (2007)
Lipton, Z.C.: The mythos of model interpretability. CoRR (2016)
Mueen, A., Keogh, E.J., Young, N.E.: Logical-shapelets: an expressive primitive for time series classification. In: Proceedings of the 17th ACM SIGKDD, San Diego, CA, USA, 21–24 August 2011, pp. 1154–1162 (2011)
Qian, L., Zheng, H., Zhou, H., Qin, R., Li, J.: Classification of time series gene expression in clinical studies via integration of biological network. PLOS ONE 1–12 (2013)
Quinlan, J.R.: Induction of decision trees. Mach. Learn. 1(1), 81–106 (1986)
Schäfer, P.: The BOSS is concerned with time series classification in the presence of noise. Data Min. Knowl. Discov. 29(6), 1505–1530 (2015)
Senin, P., Malinchik, S.: SAX-VSM: interpretable time series classification using SAX and vector space model. In: 2013 IEEE 13th International Conference on Data Mining, Dallas, TX, USA, 7–10 December 2013, pp. 1175–1180 (2013)
Yamada, Y., Suzuki, E., Yokoi, H., Takabayashi, K.: Decision-tree induction from time-series data based on a standard-example split test. In: ICML 2003, Washington, DC, USA, 21–24 August 2003, pp. 840–847 (2003)
Ye, L., Keogh, E.J.: Time series shapelets: a new primitive for data mining. In: Proceedings of the 15th ACM SIGKDD, pp. 947–956 (2009)
Acknowledgments
The study is funded by IKATS project (an Innovative Toolkit for Analysing Time Series), which is a Research and Development project funded by BPIfrance in the frame of the french national PIA program.
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Shalaeva, V., Alkhoury, S., Marinescu, J., Amblard, C., Bisson, G. (2018). Multi-operator Decision Trees for Explainable Time-Series Classification. In: Medina, J., et al. Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Foundations. IPMU 2018. Communications in Computer and Information Science, vol 853. Springer, Cham. https://doi.org/10.1007/978-3-319-91473-2_8
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DOI: https://doi.org/10.1007/978-3-319-91473-2_8
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