Adaptation of decision trees for handling concept drift

Abstract

The problem of data stream mining is widely studied in the literature. Especially difficult to solve is the problem of mining data with occurring concept drift. The most commonly used algorithms are those based on decision trees. In this article we investigate the performance of a few algorithms of constructing decision trees for data stream classification, not explicitly designed to deal with changing distribution of data. We show how to adapt these methods to deal with concept drift and we compare the obtained results. © 2013 Springer-Verlag.