Título Negative correlation learning in the extreme learning machine framework
Autores PERALES GONZÁLEZ, CARLOS, CARBONERO RUZ, MARIANO, PÉREZ RODRÍGUEZ, JAVIER, BECERRA ALONSO, DAVID, FERNÁNDEZ NAVARRO, FRANCISCO DE ASÍS, CARBONERO RUZ, MARIANO, BECERRA ALONSO, DAVID, FERNÁNDEZ NAVARRO, FRANCISCO DE ASÍS, PERALES GONZÁLEZ, CARLOS, PÉREZ RODRÍGUEZ, JAVIER
Publicación externa No
Medio Neural Comput. Appl.
Alcance Article
Naturaleza Científica
Cuartil JCR 1
Cuartil SJR 2
Ámbito Internacional
Fecha de publicacion 01/01/2020
ISI 000517702000001
DOI 10.1007/s00521-020-04788-9
Abstract Extreme learning machine (ELM) has shown to be a suitable algorithm for classification problems. Several ensemble meta-algorithms have been developed in order to generalize the results of ELM models. Ensemble approaches introduced in the ELM literature mainly come from boosting and bagging frameworks. The generalization of these methods relies on data sampling procedures, under the assumption that training data are heterogeneously enough to set up diverse base learners. The proposed ELM ensemble model overcomes this strong assumption by using the negative correlation learning (NCL) framework. An alternative diversity metric based on the orthogonality of the outputs is proposed. The error function formulation allows us to develop an analytical solution to the parameters of the ELM base learners, which significantly reduce the computational burden of the standard NCL ensemble method. The proposed ensemble method has been validated by an experimental study with a variety of benchmark datasets, comparing it with the existing ensemble methods in ELM. Finally, the proposed method statistically outperforms the comparison ensemble methods in accuracy, also reporting a competitive computational burden (specially if compared to the baseline NCL-inspired method).
Palabras clave Negative correlation learning; Extreme learning machine; Ensemble; Diversity
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