Multi-source transfer learning for non-stationary environments

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Authors

Colleges, School and Institutes

External organisations

  • University of Leicester

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. 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.

Details

Original languageEnglish
Title of host publicationProceedings of the International Joint Conference on Neural Networks (IJCNN 2019)
Publication statusAccepted/In press - 7 Mar 2019
Event International Joint Conference on Neural Networks (IJCNN 2019) - Budapest, Hungary
Duration: 14 Jul 201919 Jul 2019

Conference

Conference International Joint Conference on Neural Networks (IJCNN 2019)
CountryHungary
CityBudapest
Period14/07/1919/07/19

Keywords

  • concept drift, non-stationary environment, multi-sources, transfer learning