Multi-Source Transfer Learning for Non-Stationary Environments

Abstract--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. T o 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. Index Terms --concept drift, non-stationary environment, multi-sources, transfer learning. I NTRODUCTION Many real world applications produce data in a streaming fashion, i.e., as a sequence of observations that arrive over time. Examples include prediction of customer behaviour, credit card approval, fraud detection, software effort estimation, software defect prediction, etc. A challenge in data stream mining is how to describe a given target probability distribution accurately without knowing the whole data stream beforehand.