CHADE: Metalearning with Classifier Chains for Dynamic Combination of Classifiers

• 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: Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2016, Riva del Garda, Italy, September 19-23, 2016, Proceedings, Part I

Páginas: 410-425

Indexação

Scopus - 14 Citações

Outras Informações

ID Authenticus: P-00K-TH4

DOI: 10.1007/978-3-319-46128-1_26

Abstract (EN): Dynamic selection or combination (DSC) methods allow to select one or more classifiers from an ensemble according to the characteristics of a given test instance x. Most methods proposed for this purpose are based on the nearest neighbours algorithm: it is assumed that if a classifier performed well on a set of instances similar to x, it will also perform well on x. We address the problem of dynamically combining a pool of classifiers by combining two approaches: metalearning and multi-label classification. Taking into account that diversity is a fundamental concept in ensemble learning and the interdependencies between the classifiers cannot be ignored, we solve the multi-label classification problem by using a widely known technique: Classifier Chains (CC). Additionally, we extend a typical metalearning approach by combining metafeatures characterizing the interdependencies between the classifiers with the base-level features.We executed experiments on 42 classification datasets and compared our method with several state-of-the-art DSC techniques, including another metalearning approach. Results show that our method allows an improvement over the other metalearning approach and is very competitive with the other four DSC methods.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 15