  In data stream mining, predictive models typically suffer drops in predictive
performance due to concept drift. As enough data representing the new concept
must be collected for the new concept to be well learnt, the predictive
performance of existing models usually takes some time to recover from concept
drift. To speed up recovery from concept drift and improve predictive
performance in data stream mining, this work proposes a novel approach called
Multi-sourcE onLine TrAnsfer learning for Non-statIonary Environments
(Melanie). Melanie is the first approach able to transfer knowledge between
multiple data streaming sources in non-stationary environments. It creates
several sub-classifiers to learn different aspects from different source and
target concepts over time. The sub-classifiers that match the current target
concept well are identified, and used to compose an ensemble for predicting
examples from the target concept. We evaluate Melanie on several synthetic data
streams containing different types of concept drift and on real world data
streams. The results indicate that Melanie can deal with a variety drifts and
improve predictive performance over existing data stream learning algorithms by
making use of multiple sources.
