Heterogeneous ensemble selection for evolving data streams
作者：
Highlights：
• An online ensemble selection method that takes into account each heterogeneous base classifier's confidence during classification and its overall accuracy on the data stream is proposed.
• Each base classifier's confidence in classification for a test sample is estimated by a threshold computed dynamically using stochastic gradient descent.
• The overall accuracy of the base classifier is computed using the prequential accuracy that emphasizes more recent instances in the data stream.
• Extensive comparative experiments with the state-of-the-art algorithms on online ensemble selection validated the superior performance of our algorithm.
摘要
•An online ensemble selection method that takes into account each heterogeneous base classifier's confidence during classification and its overall accuracy on the data stream is proposed.•Each base classifier's confidence in classification for a test sample is estimated by a threshold computed dynamically using stochastic gradient descent.•The overall accuracy of the base classifier is computed using the prequential accuracy that emphasizes more recent instances in the data stream.•Extensive comparative experiments with the state-of-the-art algorithms on online ensemble selection validated the superior performance of our algorithm.
论文关键词：Data streams,Heterogeneous ensembles,Ensemble selection
论文评审过程：Received 25 November 2019, Revised 8 September 2020, Accepted 30 October 2020, Available online 2 November 2020, Version of Record 30 January 2021.
论文官网地址：https://doi.org/10.1016/j.patcog.2020.107743
