Reducing algorithm configuration spaces for efficient search

• Type: Article in International Scientific Journal
• Date: 2025

Authors

Freitas, F (Author) Other; Brazdil, P (Author) Other; Carlos Soares (Author) FEUP

Journal

Title: International Journal of Data Science and Analytics

ISSN: 2364-415X

Publisher: Springer Nature

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 0 Citations

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 21