Decision Tree Ensemble vs. N.N. Deep Learning : efficiency comparison for a small image dataset

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

This paper presents a study of the efficiency of machine learning algorithms applied on an image recognition task. The dataset is composed of aerial GeoTIFF images of 5 different vineyards taken with a drone. It presents the application of two different classification algorithms with an efficiency comparison over a small dataset. A Neural Network algorithm for classification through the TensorFlow platform will be explained first, and a Decision Tree Ensemble algorithm for classification through a machine learning platform will be explained second. This work shows that the accuracy of the Decision Tree Ensemble algorithm (94.27%) outperforms the accuracy of the Deep Learning algorithm (91.22%). This result is based on the final detection accuracy as well as on the computation time.


Type de conférence:
full paper
Faculté:
Economie et Services
Ecole:
HEG-VS
Institut:
Institut Informatique de gestion
Classification:
Informatique
Adresse bibliogr.:
Jakarta, Indonesia, 12-13 May 2018
Date:
2018-05
Jakarta, Indonesia
12-13 May 2018
Pagination:
6 p.
Veröffentlicht in:
Proceedings of International Workshop on Big Data and Information Security (IWBIS 2018)
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 Datensatz erzeugt am 2018-11-16, letzte Änderung am 2019-06-11

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