| Texto completo | |
| Autor(es): |
de Abreu, Iuri Bonna M.
;
Mantovani, Rafael G.
;
Cerri, Ricardo
;
IEEE
Número total de Autores: 4
|
| Tipo de documento: | Artigo Científico |
| Fonte: | 2017 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN); v. N/A, p. 8-pg., 2017-01-01. |
| Resumo | |
Multi-label classification is a machine learning task where instances can be classified into two or more labels simultaneously. In this task, there exist correlations between the instances belonging to same or similar sets of labels. This paper proposes the incorporation of instance correlations by modifying the multi-label datasets. We used the label-space to create new features, which represent these correlations. The original and modified datasets were used with different multi-label classification methods. Experiments have shown that better results can be obtained when instance correlations were incorporated in the classification tasks. All methods were evaluated with measures specifically designed for multi-label problems. (AU) | |
| Processo FAPESP: | 15/14300-1 - Classificação hierárquica de elementos transponíveis utilizando aprendizado de máquina |
| Beneficiário: | Ricardo Cerri |
| Modalidade de apoio: | Auxílio à Pesquisa - Regular |
| Processo FAPESP: | 12/23114-9 - Uso de meta-aprendizado para ajuste de parâmetros em problemas de classificação |
| Beneficiário: | Rafael Gomes Mantovani |
| Modalidade de apoio: | Bolsas no Brasil - Doutorado |