Mining Concept Changes by Clustering Classifiers

Abstract

The demand for learning machines that can adapt to concept change, the change over time of the statistical properties of a target variable, has become more urgent. We propose a system in which multiple online and offline classifiers are used for learning changing concepts. Experiments with synthetic concept-drifting and concept-shifting datasets show that clustering classifiers enables our proposed system to understand the sequence and similarity of past concepts.