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. Attached Files Name Description MIMEType Size Downloads Title Hyperparameter tuning for big data using Bayesian optimisation Author(s) Theckel Joy, Tinu Rana, Santu orcid.org/0000-0003-2247-850X Gupta, Sunil orcid.org/0000-0002-3308-1930 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 Document type: Conference Paper Collections: Faculty of Science, Engineering and Built Environment School of Information Technology Centre for Pattern Recognition and Data Analytics (PRADA) 2018 ERA Submission Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Mon, 01 May 2017, 13:04:17 EST Mon, 01 May 2017, 13:04:50 EST Tue, 02 May 2017, 05:00:25 EST Tue, 02 May 2017, 08:20:23 EST Sat, 27 May 2017, 02:32:15 EST Mon, 29 May 2017, 13:33:14 EST Tue, 30 May 2017, 06:06:43 EST Wed, 31 May 2017, 11:47:46 EST Wed, 31 May 2017, 11:57:02 EST Sat, 07 Oct 2017, 01:30:40 EST Filtered Full Citation counts:   Cited 8 times in TR Web of Science Cited 9 times in Scopus Search Google Scholar Access Statistics: 326 Abstract Views, 7 File Downloads  -  Detailed Statistics Created: Mon, 29 May 2017, 13:33:14 EST

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