Decision Tree Ensemble vs. N.N. Deep Learning: Efficiency Comparison for a Small Image Dataset
| Art der Publikation: | Artikel in einem Konferenzbericht |
| Zitat: | |
| Jahr: | 2018 |
| Monat: | Mai |
| Verlag: | Inproceedings of the 2018 International Workshop on Big Data and Information Security |
| Ort: | Jakarta, Indonesia |
| DOI: | 10.1109/IWBIS.2018.8471704 |
| Abriss: | 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. |
| Schlagworte: | |
| Autoren | |
| Hinzugefügt von: | [] |
| Gesamtbewertung: | 0 |
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