| 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 |
| External publication |
No |
| Means |
Neurocomputing |
| Scope |
Article |
| Nature |
Científica |
| JCR Quartile |
1 |
| SJR Quartile |
1 |
| JCR Impact |
4.43800 |
| SJR Impact |
1.17800 |
| 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 |
| Universidad Loyola members |
|
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