Hyperparameter tuning for big data using Bayesian optimisation

Theckel Joy, Tinu, Rana, Santu, Gupta, Sunil and Venkatesh, Svetha 2016, Hyperparameter tuning for big data using Bayesian optimisation, in ICPR 2016: Proceedings of the 23rd International Conference on Pattern Recognition, IEEE, Piscataway, N.J., pp. 2574-2579, doi: 10.1109/ICPR.2016.7900023.

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Title Hyperparameter tuning for big data using Bayesian optimisation
Author(s) Theckel Joy, Tinu
Rana, SantuORCID iD for Rana, Santu orcid.org/0000-0003-2247-850X
Gupta, SunilORCID iD for Gupta, Sunil orcid.org/0000-0002-3308-1930
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Conference name Pattern Recognition. International Conference (23rd : 2016 : Cancun, Mexico)
Conference location Cancun, Mexico
Conference dates 4-8 Dec. 2016
Title of proceedings ICPR 2016: Proceedings of the 23rd International Conference on Pattern Recognition
Publication date 2016
Conference series Pattern Recognition International Conference
Start page 2574
End page 2579
Total pages 6
Publisher IEEE
Place of publication Piscataway, N.J.
Summary Hyperparameters play a crucial role in the model selection of machine learning algorithms. Tuning these hyperparameters can be exhaustive when the data is large. Bayesian optimisation has emerged as an efficient tool for hyperparameter tuning of machine learning algorithms. In this paper, we propose a novel framework for tuning the hyperparameters for big data using Bayesian optimisation. We divide the big data into chunks and generate hyperparameter configurations for the chunks using the standard Bayesian optimisation. We utilise this information from the chunks for hyperparameter tuning on big data using a transfer learning setting. We evaluate the performance of the proposed method on the task of tuning hyperparameters of two machine learning algorithms. We show that our method achieves the best available hyperparameter configuration within less computational time compared to the state-of-art hyperparameter tuning methods.
ISBN 9781509048472
Language eng
DOI 10.1109/ICPR.2016.7900023
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 0 Not Applicable
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2016, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30094576

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