Active and adaptive ensemble learning for online activity recognition from data streams
作者：
Highlights：
• Novel ensemble learning algorithm for online activity recognition.
• Adaptive and accurate mining of non-stationary sensor data streams.
• One-vs-one decomposition with evolving classifiers for multi-class classification.
• Adaptive classifier weight calculation scheme.
• Active learning module to reduce the labelling cost.
摘要
•Novel ensemble learning algorithm for online activity recognition.•Adaptive and accurate mining of non-stationary sensor data streams.•One-vs-one decomposition with evolving classifiers for multi-class classification.•Adaptive classifier weight calculation scheme.•Active learning module to reduce the labelling cost.
论文关键词：Data streams,Ensemble learning,One-vs-One,Active learning,Concept drift,Activity recognition
论文评审过程：Received 20 January 2017, Revised 25 September 2017, Accepted 26 September 2017, Available online 28 September 2017, Version of Record 13 November 2017.
论文官网地址：https://doi.org/10.1016/j.knosys.2017.09.032
