Towards retraining of machine learning algorithms : an efficiency analysis applied to smart agriculture

Treboux, Jérôme (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)) ; Ingold, Rolf (University of Fribourg, Switzerland) ; Genoud, Dominique (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis))

This paper compares the efficiency of state-of-the-art machine learning algorithms used to detect an object in an image. A comparison between a deep learning algorithm such as the VGG-16 and a well-tuned random forest algorithm using classical image analysis parameters is presented. To estimate the efficiency, the classification performances like AUC, precision, recall and computation time of the algorithm retraining process are used. The experimental set-up shows that a well-tuned random forest algorithm is equal to, or better than, the deep learning approach and increases the speed of the retraining process by a factor of around 400.


Keywords:
Conference Type:
published full paper
Faculty:
Economie et Services
School:
HEG-VS
Institute:
Institut Informatique de gestion
Subject(s):
Informatique
Publisher:
Virtual conference, 3 June 2020
Date:
2020-06
Virtual conference
3 June 2020
Pagination:
6 p.
Published in:
Proceedings of 2020 Global Internet of Things Summit (GIoTS)
DOI:
ISBN:
978-1-7281-6728-2
Appears in Collection:

Note: The status of this file is: restricted


 Record created 2020-11-30, last modified 2021-02-05

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