Abstract
This paper presents a novel, generic, scalable, autonomous, and flexible supervised learning algorithm for the classification of multi-variate and variable length time series. The essential ingredients of the algorithm are randomization, segmentation of time-series, decision tree ensemble based learning of subseries classifiers, combination of subseries classification by voting, and cross-validation based temporal resolution adaptation. Experiments are carried out with this method on 10 synthetic and real-world datasets. They highlight the good behavior of the algorithm on a large diversity of problems. Our results are also highly competitive with existing approaches from the literature.
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Keywords
- Base Learner
- Multivariate Time Series
- Supervise Learning Algorithm
- Time Series Database
- Single Decision Tree
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References
Alonso González, J., Rodríguez Diez, J.J.: Boosting interval-based literals: Variable length and early classification. In: Last, M., Kandel, A., Bunke, H. (eds.) Data mining in time series databases, June 2004. World Scientific, Singapore (2004)
Geurts, P.: Pattern extraction for time-series classification. In: Siebes, A., De Raedt, L. (eds.) PKDD 2001. LNCS (LNAI), vol. 2168, pp. 115–127. Springer, Heidelberg (2001)
Geurts, P.: Contributions to decision tree induction: bias/variance tradeoff and time series classification. PhD thesis, University of Liège, Belgium (May 2002)
Geurts, P., Wehenkel, L.: Segment and combine approach for non-parametric time-series classification. Technical report, University of Liège (2005)
Hettich, S., Bay, S.D.: The UCI KDD archive. Irvine, CA: University of California, Department of Information and Computer Science (1999), http://kdd.ics.uci.edu
Kadous, M.W.: Learning comprehensible descriptions of multivariate time series. In: Proceedings of the Sixteenth International Conference on Machine Learning, ICML 1999, Bled, Slovenia, pp. 454–463 (1999)
Kadous, M.W., Sammut, C.: Classification of multivariate time series and structured data using contructive induction. Machine learning 58(1-2), 179–216 (2005)
Kudo, M., Toyama, J., Shimbo, M.: Multidimensional curve classification using passing-through regions. Pattern Recognition Letters 20(11-13), 1103–1111 (1999)
Marée, R., Geurts, P., Piater, J., Wehenkel, L.: Random subwindows for robust image classification. In: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, CVPR 2005 (2005)
Mierswa, I., Morik, K.: Automatic feature extraction for classifying audio data. Machine Learning 58(1-2), 127–149 (2005)
Olszewski, R.T.: Generalized feature extraction for structural pattern recognition in time-series data. PhD thesis, Carnegie Mellon University, Pittsburgh, PA (2001)
Ratanamahatana, C.A., Keogh, E.: Making time-series classification more accurate using learned constraints. In: Proceedings of SIAM (2004)
Shimodaira, H., Noma, K.I., Nakai, M., Sagayama, S.: Dynamic time-alignment kernel in support vector machine. Advances in Neural Information Processing Systems 14, NIPS2001 2, 921–928 (2001)
Yamada, Y., Suzuki, E., Yokoi, H., Takabayashi, K.: Decision-tree induction from time-series data based on standard-example split test. In: Proceedings of the 20th International Conference on Machine Learning, ICML 2003 (2003)
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Geurts, P., Wehenkel, L. (2005). Segment and Combine Approach for Non-parametric Time-Series Classification. In: Jorge, A.M., Torgo, L., Brazdil, P., Camacho, R., Gama, J. (eds) Knowledge Discovery in Databases: PKDD 2005. PKDD 2005. Lecture Notes in Computer Science(), vol 3721. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11564126_48
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DOI: https://doi.org/10.1007/11564126_48
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29244-9
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