Vote-boosting ensembles
作者：
Highlights：
• A boosting algorithm with a new emphasis function is proposed.
• The instance weights are determined in terms of the degree of agreement or disagreement among the individual ensemble predictions.
• The optimal type of emphasis (either on instances for which there is agreement or disagreement) can be empirically determined using cross validation.
• Vote-boosting can be used to build ensembles that are both accurate and robust to class-label noise.
摘要
•A boosting algorithm with a new emphasis function is proposed.•The instance weights are determined in terms of the degree of agreement or disagreement among the individual ensemble predictions.•The optimal type of emphasis (either on instances for which there is agreement or disagreement) can be empirically determined using cross validation.•Vote-boosting can be used to build ensembles that are both accurate and robust to class-label noise.
论文关键词：Ensemble learning,Boosting,Uncertainty-based emphasis,Robust classification
论文评审过程：Received 5 August 2017, Revised 4 May 2018, Accepted 20 May 2018, Available online 22 May 2018, Version of Record 1 June 2018.
论文官网地址：https://doi.org/10.1016/j.patcog.2018.05.022
