Weighted extreme learning machine for imbalance learning

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Domain Adaptation Extreme Learning Machines for Drift Compensation in E-Nose Systems (2015, background): “In terms of the imbalanced number of classes, a weighted ELM was proposed for binary/multiclass classification tasks with both balanced and imbalanced data distribution [6].”
An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics (2013, background): “These approaches can be categorized into two groups: the internal approaches that create new algorithms or modify existing ones to take the class-imbalance problem into consideration [7,41,82,129,152] and external approaches that preprocess the data in order to diminish the effect of their class imb”
Semi-Supervised and Unsupervised Extreme Learning Machines (2014, methodology): “for specific problems, such as ELMs for online sequential data [29]–[31], ELMs for noisy/missing data [32]–[34], ELMs for imbalanced data [35], and so on.”
A review on extreme learning machine (2021, background): “Zong and Huang [246] Weighted ELM was proposed to handle class imbalanced data.”
Artificial Intelligence and COVID-19: Deep Learning Approaches for Diagnosis and Treatment (2020, background): “There are several other studies that have previously scrutinized ELM with fixed network architectures [30], [32]–[34].”