Effect of Metalearning on Feature Selection Employment

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2017

Autores

das Dôres, SN (Autor) Outra; Carlos Soares (Autor) FEUP; Ruiz, DDA (Autor) Outra

Ata de Conferência Internacional

Título: AutoML@PKDD/ECML

Páginas: 84-90

2017 International Workshop on Automatic Selection, Configuration and Composition of Machine Learning Algorithms, AutoML 2017

22 September 2017

Indexação

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-00N-AQM

Abstract (EN): Feature Selection is important to improve learning performance, reduce computational complexity and decrease required storage. There are multiple methods for feature selection, with varying impact and computational cost. Therefore, choosing the right method for a given data set is important. In this paper, we analyze the advantages of metalearning for feature selection employment. This issue is relevant because a wrong decision may imply additional processing, when FS is unnecessarily applied, or in a loss of performance, when not used in a problem for which it is appropriate. Our results showed that, although there is an advantage in using metalearning, these gains are not yet sufficiently relevant, which opens the way for new research to be carried out in the area.

Idioma: Inglês

Tipo (Avaliação Docente): Científica