A Tree-based Concept Drift Detection Method by Three-way Decisions

Abstract

Abstract. Concept drift detection is an active research area in data stream mining. The existing concept drift detection methods usually determine a concept is drifted or not drifted. In other words, the concepts are assigned into two classes such as drifted and un-drifted, which is typically based on two-way decisions. Most likely, these methods incorrectly determine that concept drift occurs due to some uncertain factors such as noise, and some real concept drifts are not detected. Inspired by the three-way decision theory, we propose a tree-based concept drift detection method by three-way decisions in this paper, in order to improve the accuracy of detection. The basic idea of the method is to assign the concepts into three classes such as drifted, nondeterministic drifted and un-drifted. Furthermore, concepts in the class of nondeterministic drifted are determined further according to the deviation between the classification error rates. The results of comparison experiments show the effectiveness and efficiency of the proposed methods.