An Ensemble Learning Approach for Concept Drift

Abstract

Recently, concept drift has become an important issue while analyzing non-stationary distribution data in data mining. For example, data streams carry a characteristic that data vary by time, and there is probably concept drift in this type of data. Concept drifts can be categorized into sudden and gradual concept drifts in brief. Most of research only can solve one type of concept drift. However, in the real world, a data stream probably has more than one type of concept drift, and the type is usually difficult to be identified. In light of these reasons, we propose a new weighting method which can adapt more quickly to current concept than other methods and can improve the accuracy of classification on data streams with concept drifts.