| Title | A Preliminary Study of Diversity in Extreme Learning Machines Ensembles |
|---|---|
| Authors | PERALES GONZÁLEZ, CARLOS, CARBONERO RUZ, MARIANO, BECERRA ALONSO, DAVID, FERNÁNDEZ NAVARRO, FRANCISCO DE ASÍS |
| External publication | No |
| Means | Lect. Notes Comput. Sci. |
| Scope | Proceedings Paper |
| Nature | Científica |
| JCR Quartile | 4 |
| SJR Quartile | 2 |
| SJR Impact | 0.283 |
| 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 |
| Publication date | 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. |
| Keywords | Extreme learning machine; Diversity; Machine learning; Ensemble; AdaBoost |
| Universidad Loyola members |