Título A Preliminary Study of Diversity in Extreme Learning Machines Ensembles
Autores PERALES GONZÁLEZ, CARLOS, CARBONERO RUZ, MARIANO, BECERRA ALONSO, DAVID, FERNÁNDEZ NAVARRO, FRANCISCO DE ASÍS, CARBONERO RUZ, MARIANO, BECERRA ALONSO, DAVID, FERNÁNDEZ NAVARRO, FRANCISCO DE ASÍS, PERALES GONZÁLEZ, CARLOS
Publicación externa No
Medio Lect. Notes Comput. Sci.
Alcance Proceedings Paper
Naturaleza Científica
Cuartil JCR 4
Cuartil SJR 2
Impacto SJR 0.28300
Ámbito Internacional
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-85048895231&doi=10.1007%2f978-3-319-92639-1_25&partnerID=40&md5=4eae5c44e664b66d50a7600b3e8b2061
Fecha de publicacion 01/01/2018
ISI 000443487900025
Scopus Id 2-s2.0-85048895231
DOI 10.1007/978-3-319-92639-1_25
Abstract In this paper, the neural network version of Extreme Learning Machine (ELM) is used as a base learner for an ensemble meta algorithm which promotes diversity explicitly in the ELM loss function. The cost function proposed encourages orthogonality (scalar product) in the parameter space. Other ensemble-based meta-algorithms from AdaBoost family are used for comparison purposes. Both accuracy and diversity presented in our proposal are competitive, thus reinforcing the idea of introducing diversity explicitly.
Palabras clave Extreme learning machine; Diversity; Machine learning; Ensemble; AdaBoost
Miembros de la Universidad Loyola

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