A framework to decompose and develop metafeatures

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2014

Autores

Fábio Pinto (Autor) FEUP; Carlos Soares (Autor) FEUP; João Mendes-Moreira (Autor) FEUP

Ata de Conferência Internacional

Título: CEUR Workshop Proceedings

Páginas: 32-36

International Workshop on Meta-Learning and Algorithm Selection, MetaSel 2014 - Co-located with 21st European Conference on Artificial Intelligence, ECAI 2014

19 August 2014

Indexação

Scopus - 5 Citações

Classificação Científica

FOS: Ciências da engenharia e tecnologias > Engenharia electrotécnica, electrónica e informática

CORDIS: Ciências Físicas > Ciência de computadores > Cibernética > Inteligência artificial

Outras Informações

ID Authenticus: P-00A-4CZ

Abstract (EN): This paper proposes a framework to decompose and develop metafeatures for Metalearning (MtL) problems. Several metafeatures (also known as data characteristics) are proposed in the literature for a wide range of problems. Since MtL applicability is very general but problem dependent, researchers focus on generating specific and yet informative metafeatures for each problem. This process is carried without any sort of conceptual framework. We believe that such framework would open new horizons on the development of metafeatures and also aid the process of understanding the metafeatures already proposed in the state-of-the-art. We propose a framework with the aim of fill that gap and we show its applicability in a scenario of algorithm recommendation for regression problems.

Idioma: Inglês

Tipo (Avaliação Docente): Científica