Name
Abstract | ||
|---|---|---|
In this paper, we present our approach for solving the DEBS Grand Challenge 2018. The challenge asks to provide a prediction for (i) a destination and the (ii) arrival time of ships in a streaming-fashion using Geo-spatial data in the maritime context. Novel aspects of our approach include the use of ensemble learning based on Random Forest, Gradient Boosting Decision Trees (GBDT), XGBoost Trees and Extremely Randomized Trees (ERT) in order to provide a prediction for a destination while for the arrival time, we propose the use of Feed-forward Neural Networks. In our evaluation, we were able to achieve an accuracy of 97% for the port destination classification problem and 90% (in minutes) for the ETA prediction.
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Year | Venue | Field |
|---|---|---|
2018 | DEBS | Decision tree,Computer science,Event stream processing,Artificial intelligence,Artificial neural network,Random forest,Ensemble learning,Machine learning,Gradient boosting |
DocType | ISBN | Citations |
Conference | 978-1-4503-5782-1 | 1 |
PageRank | References | Authors |
0.35 | 3 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Oleh Bodunov | 1 | 1 | 0.35 |
| Florian Schmidt | 2 | 268 | 34.52 |
| André Martin | 3 | 5 | 1.49 |
| Andrey Brito | 4 | 6 | 1.37 |
| Christof Fetzer | 5 | 2429 | 172.89 |