Reducing algorithm configuration spaces for efficient search

• Tipo: Artigo em Revista Científica Internacional
• Data: 2025

Autores

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

Revista

Título: International Journal of Data Science and Analytics

ISSN: 2364-415X

Editora: Springer Nature

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-018-MD5

DOI: 10.1007/s41060-025-00764-5

Abstract (EN): Many current AutoML platforms include a very large space of alternatives (the configuration space). This increases the probability of including the best one for any dataset but makes the task of identifying it for a new dataset more difficult. 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 algorithm configuration, with limited risk of significant loss of predictive performance. We empirically validate the method with a large set of alternatives based on five ML algorithms with different sets of hyperparameters and one preprocessing method (feature selection). Our results show that it is possible to reduce the given search space by more than one order of magnitude, from a few thousands to a few hundred items. After reduction, the search for the best algorithm configuration is about one order of magnitude faster than on the original space without significant loss in predictive performance.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 21