| Texto completo | |
| Autor(es): |
Raimundo, Marcos M.
;
Von Zilben, Fernando J.
Número total de Autores: 2
|
| 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 a many-objective ensemble-based algorithm to explore the relations among the labels on multilabel classification problems. This proposal consists in two phases. In the first one, a many-objective optimization method generates a set of candidate components exploring the relations among the labels, and the second one uses a stacking method to aggregate the components for each label. By balancing or not the relevance of each label, two versions were conceived for the proposal. The balanced one presented a good performance for recall and F1 metrics, and the unbalanced one for 1-Hamming loss and precision metrics. (AU) | |
| Processo FAPESP: | 14/13533-0 - Otimização multiobjetivo em aprendizado multitarefa |
| Beneficiário: | Marcos Medeiros Raimundo |
| Modalidade de apoio: | Bolsas no Brasil - Doutorado |