PERALES GONZÁLEZ, CARLOS, CARBONERO RUZ, MARIANO, BECERRA ALONSO, DAVID, FERNÁNDEZ NAVARRO, FRANCISCO DE ASÍS

No

Lect. Notes Comput. Sci.

Proceedings Paper

Científica

✗

✗

0.283

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

01/01/2018

000443487900025

2-s2.0-85048895231

10.1007/978-3-319-92639-1_25

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.

Extreme learning machine; Diversity; Machine learning; Ensemble; AdaBoost

• DAVID BECERRA ALONSO
• MARIANO CARBONERO RUZ