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Hyperparameter tuning for big data using Bayesian optimisation

conference contribution
posted on 2016-12-08, 00:00 authored by Tinu Theckel Joy, Santu RanaSantu Rana, Sunil GuptaSunil Gupta, Svetha VenkateshSvetha Venkatesh
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.

History

Event

Pattern Recognition. International Conference (23rd : 2016 : Cancun, Mexico)

Pagination

2574 - 2579

Publisher

IEEE

Location

Cancun, Mexico

Place of publication

Piscataway, N.J.

Start date

2016-12-04

End date

2016-12-08

ISBN-13

9781509048472

Language

eng

Publication classification

E Conference publication; E1 Full written paper - refereed

Copyright notice

2016, IEEE

Title of proceedings

ICPR 2016: Proceedings of the 23rd International Conference on Pattern Recognition

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