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English Information

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Authors
# Name
1 Eduardo Silveira(esilveira@inf.ufsm.br)
2 Joaquim Assunção(joaquim@inf.ufsm.br)
3 Leonardo Emmendorfer(leonardo.emmendorfer@ufsm.br)

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Reference
# Reference
1 Bountrogiannis, K., Tzagkarakis, G., and Tsakalides, P. (2021). Data-driven kernel-based probabilistic sax for time series dimensionality reduction. In 2020 28th European Signal Processing Conference (EUSIPCO), pages 2343–2347.
2 Bountrogiannis, K., Tzagkarakis, G., and Tsakalides, P. (2022). Distribution agnostic symbolic representations for time series dimensionality reduction and online anomaly detection. IEEE Transactions on Knowledge and Data Engineering, pages 1–1.
3 Espejo, P. G., Ventura, S., and Herrera, F. (2010). A survey on the application of genetic programming to classification. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 40(2):121–144.
4 Kloska, M. and Rozinajova, V. (2020). Distribution-wise symbolic aggregate approximation (dwsax). In Intelligent Data Engineering and Automated Learning – IDEAL 2020: 21st International Conference, Guimaraes, Portugal, November 4–6, 2020, Proceedings, Part I, page 304–315, Berlin, Heidelberg. Springer-Verlag.
5 Lin, J., Keogh, E., Lonardi, S., and Chiu, B. (2003). A symbolic representation of time series, with implications for streaming algorithms. In Proceedings of the 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, DMKD ’03, page 2–11, New York, NY, USA. Association for Computing Machinery.
6 Lin, J., Keogh, E., Wei, L., and Lonardi, S. (2007). Experiencing sax: a novel symbolic representation of time series. Data Mining and knowledge discovery, 15(2):107–144.
7 Niaz, N. U., Shahariar, K. N., and Patwary, M. J. A. (2022). Class imbalance problems in machine learning: A review of methods and future challenges. In Proceedings of the 2nd International Conference on Computing Advancements, ICCA ’22, page 485–490, New York, NY, USA. Association for Computing Machinery.