Exploring the Reduction of Configuration Spaces of Workflows

• Type: Article in International Conference Proceedings Book
• Date: 2023

Authors

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

Conference proceedings International

Title: Discovery Science (DS)

Pages: 33-47

26th International Conference on Discovery Science, DS 2023

Porto, 9 October 2023 through 11 October 2023

Indexing

Scopus - 1 Citation

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 14