Decision Tree Ensemble vs. N.N. Deep Learning: Efficiency Comparison for a Small Image Dataset
| Type of publication: | Inproceedings |
| Citation: | |
| Year: | 2018 |
| Month: | May |
| Publisher: | Inproceedings of the 2018 International Workshop on Big Data and Information Security |
| Location: | Jakarta, Indonesia |
| DOI: | 10.1109/IWBIS.2018.8471704 |
| Abstract: | 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. |
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| Authors | |
| Added by: | [] |
| Total mark: | 0 |
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