Busca avançada
Ano de início
(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.)

A systematic review of multi-label feature selection and a new method based on label construction

Texto completo
Spolaor, Newton [1, 2] ; Monard, Maria Carolina [1] ; Tsoumakas, Grigorios [3] ; Lee, Huei Diana [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 Paulo - Brazil
[2] Western Parana State Univ, Lab Bioinformat, BR-85867900 Foz Do Iguacu - Brazil
[3] Aristotle Univ Thessaloniki, Machine Learning & Knowledge Discovery Grp, Dept Informat, Thessaloniki 54124 - Greece
Número total de Afiliações: 3
Tipo de documento: Artigo de Revisão
Fonte: Neurocomputing; v. 180, n. SI, p. 3-15, MAR 5 2016.
Citações Web of Science: 23

Each example in a multi-label dataset is associated with multiple labels, which are often correlated. Learning from this data can be improved when dimensionality reduction tasks, such as feature selection, are applied. The standard approach for multi-label feature selection transforms the multi-label dataset into single-label datasets before using traditional feature selection algorithms. However, this approach often ignores label dependence. In this work, we propose an alternative method, LCFS, that constructs new labels based on relations between the original labels. By doing so, the label set from the data is augmented with second-order information before applying the standard approach. To assess LCFS, an experimental evaluation using Information Gain as a measure to estimate the importance of features was carried out on 10 benchmark multi-label datasets. This evaluation compared four LCFS settings with the standard approach, using random feature selection as a reference. For each dataset, the performance of a feature selection method is estimated by the quality of the classifiers built from the data described by the features selected by the method. The results show that a simple LCFS setting gave rise to classifiers similar to, or better than, the ones built using the standard approach. Furthermore, this work also pioneers the use of the systematic review method to survey the related work on multi-label feature selection. The summary of the 99 papers found promotes the idea that exploring label dependence during feature selection can lead to good results. (C) 2015 Elsevier B.V. All rights reserved. (AU)

Processo FAPESP: 11/02393-4 - Seleção de Atributos para Aprendizado Multirrótulo
Beneficiário:Newton Spolaôr
Linha de fomento: Bolsas no Brasil - Doutorado