Feature Selection for Multi-label Learning: A Systematic Literature Review and Some Experimental Evaluations

Autor(es)

Spolaor, Newton ; Lee, Huei Diana ; Resende Takaki, Weber Shoity ; Wu, Feng Chung

• 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