Graz University of Technology, Institut für Informationssysteme und Computer Medie
Abstract
Mining data streams is a challenging task that requires online systems based
on incremental learning approaches. This paper describes a classification system
based on decision rules that may store up–to–date border examples to avoid unnecessary
revisions when virtual drifts are present in data. Consistent rules classify new test
examples by covering and inconsistent rules classify them by distance as the nearest
neighbour algorithm. In addition, the system provides an implicit forgetting heuristic
so that positive and negative examples are removed from a rule when they are not near
one another
