Selecting classification algorithms with active testing on similar datasets

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2012

Autores

Rui Leite (Autor) FEP; Pavel Brazdil (Autor) FEP; Vanschoren, J (Autor) Outra

Ata de Conferência Internacional

Título: CEUR Workshop Proceedings

Páginas: 20-27

Workshop on Ubiquitous Data Mining, UDM 2012 - In Conjunction with the 20th European Conference on Artificial Intelligence, ECAI 2012

27 August 2012 through 31 August 2012

Indexação

Scopus - 4 Citações

Outras Informações

ID Authenticus: P-00K-SPX

Abstract (EN): Given the large amount of data mining algorithms, their combinations (e.g. ensembles) and possible parameter settings, finding the most adequate method to analyze a new dataset becomes an ever more challenging task. This is because in many cases testing all possibly useful alternatives quickly becomes prohibitively expensive. In this paper we propose a novel technique, called active testing, that intelligently selects the most useful cross-validation tests. It proceeds in a tournament-style fashion, in each round selecting and testing the algorithm that is most likely to outperform the best algorithm of the previous round on the new dataset. This 'most promising' competitor is chosen based on a history of prior duels between both algorithms on similar datasets. Each new cross-validation test will contribute information to a better estimate of dataset similarity, and thus better predict which algorithms are most promising on the new dataset. We also follow a different path to estimate dataset similarity based on data characteristics. We have evaluated this approach using a set of 292 algorithm-parameter combinations on 76 UCI datasets for classification. The results show that active testing will quickly yield an algorithm whose performance is very close to the optimum, after relatively few tests. It also provides a better solution than previously proposed methods. The variants of our method that rely on crossvalidation tests to estimate dataset similarity provides better solutions than those that rely on data characteristics.

Idioma: Inglês

Tipo (Avaliação Docente): Científica