Towards Automatic Generation of Metafeatures

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2016

Autores

Pinto, F (Autor) Outra; Carlos Soares (Autor) FEUP; João Mendes-Moreira (Autor) FEUP

Ata de Conferência Internacional

Título: ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PAKDD 2016, PT I

Páginas: 215-226

20th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)

Univ Auckland, Auckland, NEW ZEALAND, APR 19-22, 2016

Indexação

ISI Web of Knowledge - 32 Citações

Scopus - 38 Citações

Classificação Científica

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

FOS: Ciências exactas e naturais > Ciências da computação e da informação

Outras Informações

ID Authenticus: P-00K-BFQ

DOI: 10.1007/978-3-319-31753-3_18

Abstract (EN): The selection of metafeatures for metalearning (MtL) is often an ad hoc process. The lack of a proper motivation for the choice of a metafeature rather than others is questionable and may originate a loss of valuable information for a given problem (e.g., use of class entropy and not attribute entropy). We present a framework to systematically generate metafeatures in the context of MtL. This framework decomposes a metafeature into three components: meta-function, object and post-processing. The automatic generation of metafeatures is triggered by the selection of a meta-function used to systematically generate metafeatures from all possible combinations of object and post-processing alternatives. We executed experiments by addressing the problem of algorithm selection in classification datasets. Results show that the sets of systematic metafeatures generated from our framework are more informative than the non-systematic ones and the set regarded as state-of-the-art.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 12