HBoost: A heterogeneous ensemble classifier based on the Boosting method and entropy measurement

作者：

Highlights：

• Heterogeneous algorithms are used for the base classifiers of the Boosting ensemble.

• Diversity and accuracy are used to prune an ensemble model.

• A self-configured ensemble model for specifying the base classifiers is addressed.

• HBoost significantly outperforms several state-of-the-art approaches.

摘要

•Heterogeneous algorithms are used for the base classifiers of the Boosting ensemble.•Diversity and accuracy are used to prune an ensemble model.•A self-configured ensemble model for specifying the base classifiers is addressed.•HBoost significantly outperforms several state-of-the-art approaches.

论文关键词：Ensemble learning,Heterogeneous models,Boosting classifier,Ensemble pruning,Ensemble diversity

论文评审过程：Received 18 December 2019, Revised 7 March 2020, Accepted 24 April 2020, Available online 28 April 2020, Version of Record 12 June 2020.

论文官网地址：https://doi.org/10.1016/j.eswa.2020.113482