| Texto completo | |
| Autor(es): |
Beserra, Fernando S.
;
Raimundo, Marcos M.
;
Von Zuben, Fernando J.
Número total de Autores: 3
|
| Tipo de documento: | Artigo Científico |
| Fonte: | PROGRESS IN PATTERN RECOGNITION, IMAGE ANALYSIS, COMPUTER VISION, AND APPLICATIONS, CIARP 2017; v. 10657, p. 9-pg., 2018-01-01. |
| Resumo | |
This paper proposes multi-objective-based classifiers to detect epileptic seizures using ensemble approaches, transfer-learning methods, and three alternative feature extraction techniques. Two aspects of the problem were investigated: (1) the relative merit of distinct proposals to synthesize an ensemble of classifiers, considering all the three feature extraction techniques; (2) the potential of an ensemble composed of transfer-learned classifiers. The blend approaches with the best performance detected all test seizures, with a high proportion of correctly detected samples inside the seizure interval and high proportion of time intervals correctly classified as non-seizures. (AU) | |
| Processo FAPESP: | 16/19080-2 - Otimização de detectores de crises epilépticas através de técnicas de aprendizado de máquina |
| Beneficiário: | Fernando dos Santos Beserra |
| Modalidade de apoio: | Bolsas no Brasil - Mestrado |
| Processo FAPESP: | 14/13533-0 - Otimização multiobjetivo em aprendizado multitarefa |
| Beneficiário: | Marcos Medeiros Raimundo |
| Modalidade de apoio: | Bolsas no Brasil - Doutorado |