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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

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

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Autor(es):
Spolaor, Newton [1, 2] ; Lee, Huei Diana [2] ; Resende Takaki, Weber Shoity [2] ; Wu, Feng Chung [2]
Número total de Autores: 4
Afiliação do(s) autor(es):
[1] Univ Sao Paulo, Lab Computat Intelligence, Inst Math & Comp Sci, BR-13560970 Sao Carlos, SP - Brazil
[2] Western Parana State Univ, Grad Program Engn & Energy Dynam Syst, Lab Bioinformat, BR-85867900 Foz Do Iguacu - Brazil
Número total de Afiliações: 2
Tipo de documento: Artigo Científico
Fonte: INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS; v. 8, n. 2, SI, p. 3-15, DEC 11 2015.
Citações Web of Science: 3
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
Linha de fomento: Bolsas no Brasil - Doutorado