Concept Drift Detection and Model Selection with Simulated Recurrence and Ensembles of Statistical Detectors

The paper presents a concept drift detection method for unsupervised learning which takes into consideration the prior knowledge to select the most appropriate classification model. The prior knowledge carries information about the data distribution patterns that reflect different concepts, which may occur in the data stream. The presented method serves as a temporary solution for a classification system after a virtual concept drift and also provides additional information about the concept data distribution for adapting the classification model. Presented detector uses a developed method called simulated recurrence and detector ensembles based on statistical tests. Evaluation is performed on benchmark datasets.