Metalearning For Pruning and Dynamic Integration In Bagging Ensembles

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

Autores

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

Ata de Conferência Internacional

Título: Proceedings of the ECMLPKDD 2015 Doctoral Consortium

Páginas: 184-187

ECMLPKDD 2015 Doctoral Consortium

Porto, Portugal, 7 de Setembro de 2015

Classificação Científica

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

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

Outras Informações

Abstract (EN): Ensemble methods have been receiving an increasing amount of attention, especially because of their successful application to problems with high visibility (e.g., the NetFlix prize, Kaggle competitions, etc). An important challenge in ensemble learning (EL) is the management of a set of models in order to ensure accuracy and computational efficiency, particularly with a large number of models in highly dynamic environments. We plan to use metalearning (MtL) to improve the performance of one of the most important EL algorithms: bagging. MtL uses data from past experiments to build models that relate the characteristics of learning problems with the behaviour of algorithms. Our approach consists in using MtL techniques that act at the level of 1) ensemble pruning and 2) ensemble integration to improve the performance of the original bagging algorithm. On the one hand, we present results of a technique that allows to prune bagging ensembles before actually generating the individual models with a performance equal to the original algorithm. On the other hand, we expose how we plan to extend the work done in 1) to include a dynamic approach. Our final goal is to achieve a MtL method that is able to select the most suitable subset of models according to the characteristics of the instance that is being predicted.

Idioma: Inglês

Tipo (Avaliação Docente): Científica