0519370 - ÚI 2020 RIV US eng C - Conference Paper (international conference)
Pešková, K. - Neruda, Roman
Hyperparameters search methods for machine learning linear workflows.
18th IEEE International Conference on Machine Learning and Applications ICMLA 2019. Proceedings. Piscataway: IEEE, 2019, s. 1205-1210. ISBN 978-1-7281-4550-1.
[ICMLA 2019: IEEE International Conference on Machine Learning and Applications /18./. Boca Raton (US), 16.12.2019-19.12.2019]
R&D Projects: GA ČR(CZ) GA18-23827S
Institutional support: RVO:67985807
Keywords : hyperparameters optimization * machine learning workflows * data preprocessing
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Hyperparameters optimization is one of the most important metalearning features that is used in AutoML systems. In this paper we use hyperparameters-space search algorithms to optimize the settings of supervised machine learning methods and workflows. We focus on changes in performance of hyperparameters optimization algorithms with the growing complexity of the hyperparameters-space, when using the data preprocessings adds more parameters to the configuration and thus more dimensions to the searched space.
Permanent Link: http://hdl.handle.net/11104/0304362