Just-in-time Adaptive Classifiers in Non-Stationary Conditions

In real world applications ageing effects, process drifts, soft and hard faults may affect the data generation mechanism and, as a consequence, data coming from it. Intelligent measurement systems developed for such processes (e.g., industrial quality assessment and control, environmental monitoring) require adaptive techniques which, by tracking the system evolution, allow the intelligent system for keeping acceptable performance. Here we focus on adaptive classifiers embedded in intelligent measurement systems designed to cope with non-stationary environments, yet well performing in stationary conditions. The novelty of the approach resides in the possibility to update in a just-in-time fashion, i.e., only when it is really needed, the knowledge base of the classifier. A large experimental campaign shows the effectiveness of the proposed design.