Citation
American Psychological Association 7th edition (APA 7th)
🇺🇸 English, US
Treboux, J., Genoud, D., & Ingold, R. (2018). Decision Tree Ensemble Vs. N.N. Deep Learning: Efficiency Comparison For A Small Image Dataset. In 2018 International Workshop on Big Data and Information Security (IWBIS) (pp. 25–30). 2018 International Workshop on Big Data and Information Security (IWBIS). IEEE. https://doi.org/10.1109/iwbis.2018.8471704
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Résumé
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.