TY  - CONF
T1  - Decision Tree Ensemble vs. N.N. Deep Learning: Efficiency Comparison for a Small Image Dataset
A1  - Treboux, Jerome
A1  - Genoud, Dominique
A1  - Ingold, Rolf
Y1  - 2018
PB  - Inproceedings of the 2018 International Workshop on Big Data and Information Security
CY  - Jakarta, Indonesia
M2  - doi: 10.1109/IWBIS.2018.8471704
N2  - 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.
ER  -