Towards Retraining of Machine Learning Algorithms: An Efficiency Analysis Applied to Smart Agriculture
| Art der Publikation: | Artikel in einem Konferenzbericht |
| Zitat: | |
| Buchtitel: | 2020 Global Internet of Things Summit (GIoTS) |
| Jahr: | 2020 |
| Monat: | Juni |
| Seiten: | 1-6 |
| Verlag: | IEEE |
| Ort: | Dublin, Ireland |
| ISBN: | 978-1-7281-6728-2 |
| DOI: | 10.1109/GIOTS49054.2020.9119601 |
| Abriss: | This paper compares the efficiency of state-of-the-art machine learning algorithms used to detect an object in an image. A comparison between a deep learning algorithm such as the VGG-16 and a well-tuned random forest algorithm using classical image analysis parameters is presented. To estimate the efficiency, the classification performances like AUC, precision, recall and computation time of the algorithm retraining process are used. The experimental set-up shows that a well-tuned random forest algorithm is equal to, or better than, the deep learning approach and increases the speed of the retraining process by a factor of around 400. |
| Schlagworte: | Active Learning, aerial images, decision tree ensemble, Image recognition, machine learning, Neural Network, Precision agriculture, Prediction, Random Forests, Real-Tme Retraining, Smart Agriculture |
| Autoren | |
| Hinzugefügt von: | [] |
| Gesamtbewertung: | 0 |
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