Fast Algorithm Selection Using Learning Curves

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

Autores

van Rijn, JN (Autor) Outra; Abdulrahman, SM (Autor) Outra; Pavel Brazdil (Autor) FEP; Vanschoren, J (Autor) Outra

Ata de Conferência Internacional

Título: Advances in Intelligent Data Analysis XIV

Páginas: 298-309

14th International Symposium on Intelligent Data Analysis (IDA)

Saint Etienne, FRANCE, OCT 22-24, 2015

Indexação

ISI Web of Knowledge - 28 Citações

Scopus - 36 Citações

Outras Informações

ID Authenticus: P-00G-T0K

DOI: 10.1007/978-3-319-24465-5_26

Abstract (EN): One of the challenges in Machine Learning to find a classifier and parameter settings that work well on a given dataset. Evaluating all possible combinations typically takes too much time, hence many solutions have been proposed that attempt to predict which classifiers are most promising to try. As the first recommended classifier is not always the correct choice, multiple recommendations should be made, making this a ranking problem rather than a classification problem. Even though this is a well studied problem, there is currently no good way of evaluating such rankings. We advocate the use of Loss Time Curves, as used in the optimization literature. These visualize the amount of budget (time) needed to converge to a acceptable solution. We also investigate a method that utilizes the measured performances of classifiers on small samples of data to make such recommendation, and adapt it so that it works well in Loss Time space. Experimental results show that this method converges extremely fast to an acceptable solution.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 12