title: Just-in-time classifiers for recurrent concepts.
abstract: Just-in-time (JIT) classifiers operate in evolving environments by classifying instances and reacting to concept drift. In stationary conditions, a JIT classifier improves its accuracy over time by exploiting additional supervised information coming from the field. In nonstationary conditions, however, the classifier reacts as soon as concept drift is detected; the current classification setup is discarded and a suitable one activated to keep the accuracy high. We present a novel generation of JIT classifiers able to deal with recurrent concept drift by means of a practical formalization of the concept representation and the definition of a set of operators working on such representations. The concept-drift detection activity, which is crucial in promptly reacting to changes exactly when needed, is advanced by considering change-detection tests monitoring both inputs and classes distributions.
authors.forename: Cesare
authors.initials: C
authors.lastname: Alippi
authors.forename: Giacomo
authors.initials: G
authors.lastname: Boracchi
authors.forename: Manuel
authors.initials: M
authors.lastname: Roveri