Deferral classification of evolving temporal dependent data streams

Abstract

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.