A common trait of all current approaches, in fact, is the reliance on batch learning techniques, which assume a "static" nature of the world where new unseen instances that will be encountered will be similar to the training data.
as a final analysis of our results, we investigated how the performance of the different types of models (batch, adaptive, empty) relates to the distance between training and test sets.