Online Adaptation in Learning Classifier Systems: Stream Data Mining

In data mining, concept drift refers to the phenomenon that the underlying model (or concept) is changing over time. The aim of this paper is twofold. First, we propose a fundamental characterization and quantification of different types of concept drift. The proposed theory enables a rigorous investigation of learning system performance on streamed data. In particular , we investigate the impact of different amounts and types of concept drift on evolutionary classification systems focusing on the learning classifier system approach. We compare performance of one Pittsburgh-type system, GAssist, which learns in batch mode using windowing techniques, with a Michigan-type system, XCS, which is a natural online learner. The results show that both systems are able to handle the various concept drifts well. Behavioral differences are discussed revealing task dependencies, representation dependencies as well as dynamics dependencies. Discussions and conclusions outline the path towards more detailed measures for problem dynamics in the data mining realm.