PERALES GONZÁLEZ, CARLOS, CARBONERO RUZ, MARIANO, BECERRA ALONSO, DAVID, PÉREZ RODRÍGUEZ, JAVIER, FERNÁNDEZ NAVARRO, FRANCISCO DE ASÍS
No
Neurocomputing
Article
Científica
4.438
1.178
07/10/2019
000480413200020
2-s2.0-85069590641
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.
Extreme Learning Machine; Ensemble; Hierarchy; Diversity; Negative Correlation