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Just-In-Time Classifiers for Recurrent Concepts

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3 Author(s)
Cesare Alippi ; Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, Italy ; Giacomo Boracchi ; Manuel Roveri

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.

Published in:

IEEE Transactions on Neural Networks and Learning Systems  (Volume:24 ,  Issue: 4 )