1. Doshi-Velez, F., Kim, B.: Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608 (2017)
2. Yin, H., Yang, S., Zhu, X., Ma, S., Chen, L.: Symbolic representation based on trend features for biomedical data classification. Technol. Health Care 23(s2), S501–S510 (2015)
3. Barnaghi, P.M., Bakar, A.A., Othman, Z.A.: Enhanced symbolic aggregate approximation method for financial time series data representation. In: 2012 6th International Conference on New Trends in Information Science, Service Science and Data Mining (ISSDM2012), pp. 790–795. IEEE (2012)
4. Kolozali, Ş., Puschmann, D., Bermudez-Edo, M., Barnaghi, P.: On the effect of adaptive and nonadaptive analysis of time-series sensory data. IEEE Internet Things J. 3(6), 1084–1098 (2016)
5. Bifet, A.: Adaptive Stream Mining: Pattern Learning and Mining from Evolving Data Streams, vol. 207. IOS Press, Amsterdam (2010)