Pruning bagging ensembles with metalearning

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2015

Autores

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

Ata de Conferência Internacional

Título: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Páginas: 64-75

12th International Workshop on Multiple Classifier Systems, MCS 2015

29 June 2015 through 1 July 2015

Indexação

ISI Web of Knowledge - 3 Citações

Scopus - 4 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-00G-69C

DOI: 10.1007/978-3-319-20248-8_6

Abstract (EN): Ensemble learning algorithms often benefit from pruning strategies that allow to reduce the number of individuals models and improve performance. In this paper, we propose a Metalearning method for pruning bagging ensembles. Our proposal differs from other pruning strategies in the sense that allows to prune the ensemble before actually generating the individual models. The method consists in generating a set characteristics from the bootstrap samples and relate them with the impact of the predictive models in multiple tested combinations. We executed experiments with bagged ensembles of 20 and 100 decision trees for 53 UCI classification datasets. Results show that our method is competitive with a state-of-the-art pruning technique and bagging, while using only 25% of the models. AG Springer International Publishing Switzerland 2015.

Idioma: Inglês

Tipo (Avaliação Docente): Científica