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...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Natural computing Jg. 20; H. 1; S. 11 - 22
Hauptverfasser:	Cheng, Jian, Chen, Jingjing, Guo, Yi-nan, Cheng, Shi, Yang, Linkai, Zhang, Pei
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Dordrecht Springer Netherlands 01.03.2021 Springer Nature B.V
Schlagworte:	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
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!