Concept Drift Detection using Supervised Bivariate Grids

We present an on-line method for concept change detection on labeled data streams. Our detection method uses a bivariate supervised criterion to determine if the data in two windows come from the same distribution. Our method has no assumption neither on data distribution nor on change type. It has the ability to detect changes of different kinds (mean, variance ... ). Experiments show that our method performs better than well-known methods from the literature. Additionally, except from the window sizes, no user parameter is required in our method.