2005
Authors
Jorge, A; Torgo, L; Brazdil, P; Camacho, R; Gama, J;
Publication
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Abstract
2023
Authors
Muhammad, SH; Abdulmumin, I; Ayele, AA; Ousidhoum, N; Adelani, DI; Yimam, SM; Ahmad, IS; Beloucif, M; Mohammad, S; Ruder, S; Hourrane, O; Brazdil, P; António Ali, FDM; David, D; Osei, S; Bello, BS; Ibrahim, F; Gwadabe, T; Rutunda, S; Belay, TD; Messelle, WB; Balcha, HB; Chala, SA; Gebremichael, HT; Opoku, B; Arthur, S;
Publication
CoRR
Abstract
2018
Authors
Sarmento, RP; Brazdil, P;
Publication
CoRR
Abstract
2022
Authors
Sarmento, RP; Cardoso, DdO; Gama, J; Brazdil, P;
Publication
CoRR
Abstract
2018
Authors
Cordeiro, M; Sarmento, RP; Brazdil, P; Gama, J;
Publication
CoRR
Abstract
2023
Authors
Freitas, F; Brazdil, P; Soares, C;
Publication
Discovery Science - 26th International Conference, DS 2023, Porto, Portugal, October 9-11, 2023, Proceedings
Abstract
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.
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