UCI plus plus : Improved Support for Algorithm Selection Using Datasetoids

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2009

Autores

Carlos Soares (Autor) FEP

Ata de Conferência Internacional

Título: Advances in Knowledge Discovery and Data Mining

Páginas: 499-506

13th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2009)

Bangkok, Thailand, April 27-30, 2009

Indexação

ISI Web of Knowledge - 17 Citações

Scopus - 22 Citações

Classificação Científica

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

Outras Informações

ID Authenticus: P-003-NXP

DOI: 10.1007/978-3-642-01307-2_46

Abstract (EN): As companies employ a larger number of models, the problem of algorithm (and parameter) selection is becoming increasingly important. Two approaches to obtain empirical knowledge that is useful for that purpose are empirical studies and metalearning. However, most empirical (meta)knowledge is obtained from a, relatively small set, of datasets. In this paper, we propose a method to obtain a large number of datasets which is based on a simple transformation of existing datasets, referred to as datasetoids. We test our approach on the problem of using metalearning to predict when to prune decision trees. The results show significant; improvement when using datasetoids. Additionally, we identify a number of potential anomalies in the generated datasetoids and propose methods to solve them.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 8