Title Regularized ensemble neural networks models in the Extreme Learning Machine framework
Authors PERALES GONZÁLEZ, CARLOS, CARBONERO RUZ, MARIANO, BECERRA ALONSO, DAVID, PÉREZ RODRÍGUEZ, JAVIER, FERNÁNDEZ NAVARRO, FRANCISCO DE ASÍS, CARBONERO RUZ, MARIANO, BECERRA ALONSO, DAVID, FERNÁNDEZ NAVARRO, FRANCISCO DE ASÍS, PERALES GONZÁLEZ, CARLOS, PÉREZ RODRÍGUEZ, JAVIER
External publication No
Means Neurocomputing
Scope Article
Nature Científica
JCR Quartile 1
SJR Quartile 1
JCR Impact 4.43800
Area International
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-85069590641&doi=10.1016%2fj.neucom.2019.06.040&partnerID=40&md5=1bdddfc5ee18c8f890cb02e0bfd02b15
Publication date 07/10/2019
ISI 000480413200020
Scopus Id 2-s2.0-85069590641
DOI 10.1016/j.neucom.2019.06.040
Abstract Extreme Learning Machine (ELM) has proven to be an efficient and speedy algorithm for classification. In order to generalize the results of standard ELM, several ensemble meta-algorithms have been implemented. On this manuscript, we propose a hierarchical ensemble methodology that promotes diversity among the elements of an ensemble, explicitly through the loss function in the single-hidden-layer feed-forward network version of ELM. The diversity term in the loss function is justified using the concept of regularization from the Negative Correlation Learning framework. Statistical tests show that our proposal is competitive in both performance and diversity measures against bagging and boosting ensemble methodologies. (C) 2019 Elsevier B.V. All rights reserved.
Keywords Extreme Learning Machine; Ensemble; Hierarchy; Diversity; Negative Correlation
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