Adaptive CCR-ELM with variable-length brain storm optimization algorithm for class-imbalance learning

Class-specific cost regulation extreme learning machine (CCR-ELM) can effectively deal with the class imbalance problems. However, its key parameters, including the number of hidden nodes, the input weights, the biases and the tradeoff factors are normally generated randomly or preset by human. More...

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Vydané v:	Natural computing Ročník 20; číslo 1; s. 11 - 22
Hlavní autori:	Cheng, Jian, Chen, Jingjing, Guo, Yi-nan, Cheng, Shi, Yang, Linkai, Zhang, Pei
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Dordrecht Springer Netherlands 01.03.2021 Springer Nature B.V
Predmet:	Adaptive algorithms Artificial Intelligence Artificial neural networks Belt conveyors Classification Complex Systems Computer Science Evolutionary algorithms Evolutionary Biology Fault diagnosis Machine learning Neural networks Nodes Optimization Optimization algorithms Parameters Processor Architectures Theory of Computation Brain storm optimization algorithm Variable-length Class-specific cost regulation extreme learning machine The class imbalance problems Adaptive
ISSN:	1567-7818, 1572-9796
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