Concept drift detection via competence models

Detecting changes of concepts, such as a change of customer preference for telecom services, is very important in terms of prediction and decision applications in dynamic environments. In particular, for case-based reasoning systems, it is important to know when and how concept drift can effectively assist decision makers to perform smarter maintenance operations at an appropriate time. This paper presents a novel method for detecting concept drift in a case-based reasoning system. Rather than measuring the actual case distribution, we introduce a new competence model that detects differences through changes in competence. Our competence-based concept detection method requires no prior knowledge of case distribution and provides statistical guarantees on the reliability of the changes detected, as well as meaningful descriptions and quantification of these changes. This research concludes that changes in data distribution do reflect upon competence. Eight sets of experiments under three categories demonstrate that our method effectively detects concept drift and highlights drifting competence areas accurately. These results directly contribute to the research that tackles concept drift in case-based reasoning, and to competence model studies.