MARLINE: Multi-Source Mapping Transfer Learning for Non-Stationary Environments
This addresses concept drift in data stream mining systems, which is a problem for online learning applications, and is incremental as it builds on existing multi-source transfer learning methods.
The paper tackles concept drift in online learning by proposing MARLINE, a method that transfers knowledge from multiple data sources even when source and target concepts do not match, achieving higher accuracy than state-of-the-art approaches on synthetic and real-world datasets.
Concept drift is a major problem in online learning due to its impact on the predictive performance of data stream mining systems. Recent studies have started exploring data streams from different sources as a strategy to tackle concept drift in a given target domain. These approaches make the assumption that at least one of the source models represents a concept similar to the target concept, which may not hold in many real-world scenarios. In this paper, we propose a novel approach called Multi-source mApping with tRansfer LearnIng for Non-stationary Environments (MARLINE). MARLINE can benefit from knowledge from multiple data sources in non-stationary environments even when source and target concepts do not match. This is achieved by projecting the target concept to the space of each source concept, enabling multiple source sub-classifiers to contribute towards the prediction of the target concept as part of an ensemble. Experiments on several synthetic and real-world datasets show that MARLINE was more accurate than several state-of-the-art data stream learning approaches.