Supervised classification is one of the main computational tasks of modern Artificial Intelligence, and it is used to automatically extract an underlying theory from a set of already classified instances. The available learning schemata are mostly limited to static instances, in which the temporal component of the information is absent, neglected, or abstracted into atemporal data, and purely, native temporal classification is still largely unexplored. In this paper, we propose a temporal rulebased classifier based on interval temporal logic, that is able to learn a classification model for multivariate classified (abstracted) time series, and we discuss some implementation issues.
Lucena-Sánchez, E., Muñoz-Velasco, E., Sciavicco, G., Stan, I., Vaccari, A. (2020). Towards Interval Temporal Logic Rule-Based Classification. In Proceedings of the 1st Workshop on Artificial Intelligence and Formal Verification, Logic, Automata, and Synthesis co-located with the 18th International Conference of the Italian Association for Artificial Intelligence (AI*IA 2019) (pp.65-70). CEUR-WS.
Towards Interval Temporal Logic Rule-Based Classification
Stan, IE;
2020
Abstract
Supervised classification is one of the main computational tasks of modern Artificial Intelligence, and it is used to automatically extract an underlying theory from a set of already classified instances. The available learning schemata are mostly limited to static instances, in which the temporal component of the information is absent, neglected, or abstracted into atemporal data, and purely, native temporal classification is still largely unexplored. In this paper, we propose a temporal rulebased classifier based on interval temporal logic, that is able to learn a classification model for multivariate classified (abstracted) time series, and we discuss some implementation issues.
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