Carlos Andres Ferrero; Luis Otavio Alvares; Willian Zalewski; Vania Bogorny.
In: ACMSAC, 2018. Proceedings of the 33 Symposium on Applied Computing, Pau, France, April 9-13, 2018
- publication: https://dl.acm.org/doi/10.1145/3167132.3167225
- code: https://github.com/bigdata-ufsc/ferrero-2018-movelets
- data:
Hurricanes
Animals
Geolife
Movelets finds the best multidimensional subsequences that discriminate the class. It is a generic method that works for any sequencial data classification problem, being able to deals the dimensions space, time, semantics and others. Movelets uses local features (subsequences) and global features extracted from the entire sequence.