Bi-directional online transfer learning: a framework
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Bi-directional online transfer learning: a framework Helen McKay1
· Nathan Griffiths1 · Phillip Taylor1 · Theo Damoulas1,2 · Zhou Xu3
Received: 5 November 2019 / Accepted: 1 June 2020 / Published online: 6 October 2020 © The Author(s) 2020
Abstract Transfer learning uses knowledge learnt in source domains to aid predictions in a target domain. When source and target domains are online, they are susceptible to concept drift, which may alter the mapping of knowledge between them. Drifts in online environments can make additional information available in each domain, necessitating continuing knowledge transfer both from source to target and vice versa. To address this, we introduce the Bi-directional Online Transfer Learning (BOTL) framework, which uses knowledge learnt in each online domain to aid predictions in others. We introduce two variants of BOTL that incorporate model culling to minimise negative transfer in frameworks with high volumes of model transfer. We consider the theoretical loss of BOTL, which indicates that BOTL achieves a loss no worse than the underlying concept drift detection algorithm. We evaluate BOTL using two existing concept drift detection algorithms: RePro and ADWIN. Additionally, we present a concept drift detection algorithm, Adaptive Windowing with Proactive drift detection (AWPro), which reduces the computation and communication demands of BOTL. Empirical results are presented using two data stream generators: the drifting hyperplane emulator and the smart home heating simulator, and real-world data predicting Time To Collision (TTC) from vehicle telemetry. The evaluation shows BOTL and its variants outperform the concept drift detection strategies and the existing state-of-the-art online transfer learning technique. Keywords Online learning · Transfer learning · Concept drift
1 Introduction Online learning (OL) is an important field of machine learning research which allows supervised learning to be conducted on data streams [9, 30]. Learning from data streams can be challenging, particularly in environments that are non-stationary in their nature, which can cause concept drift [9]. Concept drift occurs when the underlying concept changes over time, causing changes to the distribution of data, and requires predictive models to be updated or discarded to maintain effective predictions. To build accurate models, many real-world applications require large amounts of training data, which is often limited if concept drifts occur [24]. Helen McKay
[email protected] 1
Department of Computer Science, University of Warwick, Coventry, UK
2
Department of Statistics, University of Warwick, Coventry, UK
3
Jaguar Land Rover Research, Coventry, UK
Transfer learning (TL) is another prominent field of machine learning research, which allows models to be learnt in domains where training data is readily available, and used where it is limited to build more effective predictors [24]. TL has typically been conducted offline, limiting its use in real-world online environments [36]. It may be
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