| Texto completo | |
| Autor(es): |
Spolaor, Newton
;
Lee, Huei Diana
;
Resende Takaki, Weber Shoity
;
Wu, Feng Chung
Número total de Autores: 4
|
| Tipo de documento: | Artigo Científico |
| Fonte: | INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS; v. 8, p. 13-pg., 2015-12-11. |
| Resumo | |
Feature selection can remove non-important features from the data and promote better classifiers. This task, when applied to multi-label data where each instance is associated with a set of labels, supports emerging applications. Although multi-label data usually exhibit label relations, label dependence has been little studied in feature selection. We proposed two multi-label feature selection algorithms that consider label relations. These methods were experimentally competitive with traditional approaches. Moreover, this work conducted a systematic literature review, summarizing 74 related papers. (AU) | |
| Processo FAPESP: | 11/02393-4 - Seleção de Atributos para Aprendizado Multirrótulo |
| Beneficiário: | Newton Spolaôr |
| Modalidade de apoio: | Bolsas no Brasil - Doutorado |