A clustering based ensemble of weighted kernelized extreme learning machine for class imbalance learning
作者：
Highlights：
• Ensemble based classification for class imbalance learning.
• Decomposition of single classification problem into multiple sub-problems.
• Determines optimal number of sub-problems using Calinski–Harabasz Criterion.
• Uses number of probability distributions in the problem for decomposing a problem into sub-problems.
• An ensemble of Weighted Extreme Learning Machine with Gaussian kernel.
摘要
•Ensemble based classification for class imbalance learning.•Decomposition of single classification problem into multiple sub-problems.•Determines optimal number of sub-problems using Calinski–Harabasz Criterion.•Uses number of probability distributions in the problem for decomposing a problem into sub-problems.•An ensemble of Weighted Extreme Learning Machine with Gaussian kernel.
论文关键词：Classification,Class imbalance learning,Extreme learning machine,Ensemble method,Problem decomposition
论文评审过程：Received 23 May 2019, Revised 6 September 2020, Accepted 19 September 2020, Available online 22 September 2020, Version of Record 23 September 2020.
论文官网地址：https://doi.org/10.1016/j.eswa.2020.114041
