Feb 15, 2019 · This work develops a novel UnderBagging based kernelized ELM (UBKELM) to address the class imbalance problem more effectively.
Feb 15, 2019 · This work develops a novel UnderBagging based kernelized ELM (UBKELM) to address the class imbalance problem more effectively.
Traditional extreme learning machine (ELM) and Support Vector Machine (SVM) provides equal importance to all the samples leading to results biased towards the ...
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Extreme learning machine (ELM) is one of the foremost capable, quick genuine esteemed classification algorithm with good generalization performance.
UnderBagging based reduced Kernelized weighted extreme ...
www.researchgate.net › publication › 32...
Class imbalance problem happens when the training dataset contains significantly fewer instances of one class compared to another class. Traditional ...
This paper proposes a novel hybrid framework called Reduced-Kernel Weighted Extreme Learning Machine Using Universum Data in Feature Space (RKWELM-UFS)
这项工作通过对多数类样本的随机欠采样创建了几个平衡的训练子集。训练子集的数量取决于类不平衡的程度。这项工作使用核化ELM 作为组件分类器，使集成稳定 ...
Class imbalance learning using UnderBagging based kernelized ...
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... with your co-authors. Class imbalance learning using UnderBagging based kernelized extreme learning machine. Bhagat Singh Raghuwanshi, Sanyam Shukla. Class ...
Under Bagging based Kernel ELM i.e.. UBKELM is a developed variant of the Extreme Learning Machine (ELM) developed to solve the problem of class imbalance. The ...
This work designs a novel BalanceCascade-based kernelized extreme learning machine (KELM) as the base learner to build the ensemble as it is stable and has ...