  Real-world applications have been dealing with large amounts of data that
arrive over time and generally present changes in their underlying joint
probability distribution, i.e., concept drift. Concept drift can be subdivided
into two types: virtual drift, which affects the unconditional probability
distribution p(x), and real drift, which affects the conditional probability
distribution p(y|x). Existing works focuses on real drift. However, strategies
to cope with real drift may not be the best suited for dealing with virtual
drift, since the real class boundaries remain unchanged. We provide the first
in depth analysis of the differences between the impact of virtual and real
drifts on classifiers' suitability. We propose an approach to handle both
drifts called On-line Gaussian Mixture Model With Noise Filter For Handling
Virtual and Real Concept Drifts (OGMMF-VRD). Experiments with 7 synthetic and 3
real-world datasets show that OGMMF-VRD obtained the best results in terms of
average accuracy, G-mean and runtime compared to existing approaches. Moreover,
its accuracy over time suffered less performance degradation in the presence of
drifts.
