autoBagging: Learning to Rank Bagging Workflows with Metalearning

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2017

Autores

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

Ata de Conferência Internacional

Título: Proceedings of the International Workshop on Automatic Selection, Configuration and Composition of Machine Learning Algorithms co-located with the European Conference on Machine Learning & Principles and Practice of Knowledge Discovery in Databases, AutoML@PKDD/ECML 2017, Skopje, Macedonia, September 22, 2017.

Páginas: 21-27

2017 International Workshop on Automatic Selection, Configuration and Composition of Machine Learning Algorithms, AutoML 2017

22 September 2017

Indexação

Scopus - 3 Citações

Outras Informações

ID Authenticus: P-00M-YFM

Abstract (EN): Machine Learning (ML) has been successfully applied to a wide range of domains and applications. One of the techniques behind most of these successful applications is Ensemble Learning (EL), the field of ML that gave birth to methods such as Random Forests or Boosting. The complexity of applying these techniques together with the market scarcity on ML experts, has created the need for systems that enable a fast and easy drop-in replacement for ML libraries. Automated machine learning (autoML) is the field of ML that attempts to answers these needs. We propose autoBagging, an autoML system that automatically ranks 63 bagging workflows by exploiting past performance and metalearning. Results on 140 classification datasets from the OpenML platform show that autoBagging can yield better performance than the Average Rank method and achieve results that are not statistically different from an ideal model that systematically selects the best workflow for each dataset. For the purpose of reproducibility and generalizability, autoBagging is publicly available as an R package on CRAN.

Idioma: Inglês

Tipo (Avaliação Docente): Científica