ReliefF for Multi-label Feature Selection

Autor(es)

Spolaor, Newton ; Cherman, Everton Alvares ; Monard, Maria Carolina ; Lee, Huei Diana ; Pozo, ATR ; Camargo, HD ; Furtado, V ; Pinheiro, V

• Tipo de documento: Artigo Científico
• Fonte: 2013 BRAZILIAN CONFERENCE ON INTELLIGENT SYSTEMS (BRACIS); v. N/A, p. 6-pg., 2013-01-01.

Resumo

The feature selection process aims to select a subset of relevant features to be used in model construction, reducing data dimensionality by removing irrelevant and redundant features. Although effective feature selection methods to support single-label learning are abound, this is not the case for multi-label learning. Furthermore, most of the multilabel feature selection methods proposed initially transform the multi-label data to single-label in which a traditional feature selection method is then applied. However, the application of single-label feature selection methods after transforming the data can hinder exploring label dependence, an important issue in multi-label learning. This work proposes a new multi-label feature selection algorithm, RF-ML, by extending the single-label feature selection ReliefF algorithm. RF-ML, unlike strictly univariate measures for feature ranking, takes into account the effect of interacting attributes to directly deal with multilabel data without any data transformation. Using synthetic datasets, the proposed algorithm is experimentally compared to the ReliefF algorithm in which the multi-label data has been previously transformed to single-label data using two well-known data transformation approaches. Results show that the proposed algorithm stands out by ranking the relevant features as the best ones more often. (AU)

• Processo FAPESP: 11/02393-4 - Seleção de Atributos para Aprendizado Multirrótulo
• Beneficiário: Newton Spolaôr
• Modalidade de apoio: Bolsas no Brasil - Doutorado
• Processo FAPESP: 10/15992-0 - Explorando a dependência de rótulos no aprendizado multirrótulo
• Beneficiário: Everton Alvares Cherman
• Modalidade de apoio: Bolsas no Brasil - Doutorado Direto