An Empirical Insight Into Concept Drift Detectors Ensemble Strategies

Contemporary decision support systems have to take into consideration the fact that most of data gathered is nowadays in motion, i.e., that successive observations, about objects being analyzed, form so-called data streams. Unfortunately, during analytical model utilization, unpredictable changes may appear in data distributions, leading to significant deterioration in the predictive performance and reliability of these learners. This phenomenon is called concept drift and refers to changes in the input data in relation to target variable in supervised learning task. Due to its potentially catastrophic impact on the underlying learner, it must be detected and handled as soon as it occurs. Over the years, many methods have been developed to address this issue. We focus on supervised classification task, aiming at answering the question on how to detect significant changes in data distribution effectively using ensemble of drift detectors. We discuss several models of combined drift detectors, among them the local detector which analyses distribution of each attribute separately. Experimental evaluations confirm the effectiveness of ensemble detectors, making them highly interesting to be used in solving real-world problems.