Exploring the Reduction of Configuration Spaces of Workflows

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2023

Autores

Freitas, F (Autor) Outra; Pavel Brazdil (Autor) FEP; Carlos Soares (Autor) FEUP

Ata de Conferência Internacional

Título: Discovery Science (DS)

Páginas: 33-47

26th International Conference on Discovery Science, DS 2023

Porto, 9 October 2023 through 11 October 2023

Indexação

Scopus - 1 Citação

Outras Informações

ID Authenticus: P-00Z-543

DOI: 10.1007/978-3-031-45275-8_3

Abstract (EN): Many current AutoML platforms include a very large space of alternatives (the configuration space) that make it difficult to identify the best alternative for a given dataset. In this paper we explore a method that can reduce a large configuration space to a significantly smaller one and so help to reduce the search time for the potentially best workflow. We empirically validate the method on a set of workflows that include four ML algorithms (SVM, RF, LogR and LD) with different sets of hyperparameters. Our results show that it is possible to reduce the given space by more than one order of magnitude, from a few thousands to tens of workflows, while the risk that the best workflow is eliminated is nearly zero. The system after reduction is about one order of magnitude faster than the original one, but still maintains the same predictive accuracy and loss. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 14