RCD: A recurring concept drift framework

This paper presents recurring concept drifts (RCD), a framework that offers an alternative approach to handle data streams that suffer from recurring concept drifts (on-line learning). It creates a new classifier to each context found and stores a sample of data used to build it. When a new concept drift occurs, the algorithm compares the new context to previous ones using a non-parametric multivariate statistical test to verify if both contexts come from the same distribution. If so, the corresponding classifier is reused. The RCD framework is compared with several algorithms (among single and ensemble approaches), in both artificial and real data sets, chosen from frequently used algorithms and data sets in the concept drift research area. We claim the proposed framework had better average ranks in data sets with abrupt and gradual concept drifts compared to both the single classifiers and the ensemble approaches that use the same base learner.

► RCD + six different approaches tested in two artificial and two real-world data sets.
► Multivariate non-parametric statistical tests used to identify reoccurring contexts.
► RCD results are better or similar in all tested data sets.
► RCD results are more stable in the selected data sets.