I. INTRODUCTION eal world industrial and environmental processes are prone to non-stationary phenomena induced by ageing effects, drifts, soft or hard faults inducing a change over time of the probability density function of acquired measurements

eal world industrial and environmental processes are prone to non-stationary phenomena induced by ageing effects, drifts, soft or hard faults inducing a change over time of the probability density function of acquired measurements [1]. As a consequence, classification systems built over these processes cannot be granted to work properly since the stationarity hypothesis assumed during the parameter configuration phase a priori does not hold any more. The changes, or concept drift, might degrade the accuracy of the classification system up to a point that the expected quality of service of the envisaged application is impaired. As stated in [2], concept drifts can be grouped into two main families: abrupt and gradual. The former type refers to situations where changes can be modeled as step-like changes affecting the environment in which the classification system is deployed. The latter models situations where the process slowly evolves over time, for example, due to ageing effects or degradation of the sensors, e.g., due to temperature and humidity. The need to deal with concept drifts [1], [2] has pushed the research toward the development of classification systems able to work in nonstationary environments by