Learning from non-stationary data using a growing network of prototypes

Learning from non-stationary data requires methods that are able to deal with a continuous stream of data instances, possibly of infinite size, where the class distributions are potentially drifting over time. For handling such datasets, we are proposing a new method that incrementally creates and adapts a network of prototypes for classifying complex data received in an online fashion. The algorithm includes both an accuracy-based and time-based forgetting mechanisms that ensure that the model size does not grow indefinitely with large datasets. We have performed tests on seven benchmarking datasets for comparing our proposal with several approaches found in the literature, including ensemble algorithms associated to two different base classifiers. Performances obtained show that our algorithm is comparable to the best of the ensemble classifiers in terms of accuracy/time trade-off. Moreover, our approach appears to have significant advantages for dealing with data that has a complex, non-linearly separable topology.