Minimum variance-embedded kernelized extension of extreme learning machine for imbalance learning

作者：

Highlights：

• Handle the class imbalance problems.

• Minimum variance embedded-kernelized weighted ELM (MVKWELM).

• Minimum variance-embedded class-specific kernelized ELM (MVCSKELM).

• The comparable training time than kernelized weighted ELM.

• Benchmark results validate effectiveness of the proposed methods.

摘要

•Handle the class imbalance problems.•Minimum variance embedded-kernelized weighted ELM (MVKWELM).•Minimum variance-embedded class-specific kernelized ELM (MVCSKELM).•The comparable training time than kernelized weighted ELM.•Benchmark results validate effectiveness of the proposed methods.

论文关键词：Extreme learning machine,Minimum variance-embedded class-specific kernelized extreme learning machine,Class imbalance problem,Classification

论文评审过程：Received 26 July 2020, Revised 30 April 2021, Accepted 16 May 2021, Available online 27 May 2021, Version of Record 6 June 2021.

论文官网地址：https://doi.org/10.1016/j.patcog.2021.108069