Mayo, M., & Bifet, A. (2016). Deferral classification of evolving temporal dependent data streams. In Proceedings of the 31st Annual ACM Symposium on Applied Computing, New York, NY, USA, April 4-8, 2016 (pp. 952–954). New York, NY, USA: ACM. http://doi.org/10.1145/2851613.2851890
Permanent Research Commons link: http://hdl.handle.net/10289/10549
Data streams generated in real-time can be strongly temporally dependent. In this case, standard techniques where we suppose that class labels are not correlated may produce sub-optimal performance because the assumption is incorrect. To deal with this problem, we present in this paper a new algorithm to classify temporally correlated data based on deferral learning. This approach is suitable for learning over time-varying streams. We show how simple classifiers such as Naive Bayes can boost their performance using this new meta-learning methodology. We give an empirical validation of our new algorithm over several real and artificial datasets.
This is an author’s accepted version of an article published in the Proceedings of the 31st Annual ACM Symposium on Applied Computing. © 2016 copyright with the authors.