Trans RINA, Vol 157, Part A3, Intl J Maritime Eng, Jul-Sep 2015


result is 95.19% for partitioning, 70% for training and 30% for testing set.


One of the advantages of the BNs approach is that it is possible to include human knowledge in the model, and that human can validate the model. However, how this should be implemented requires further investigation. This advantage has not been explored in our experiment.


Further work is also needed to evaluate other machine learning approaches on the AIS data to identify vessel anomaly behaviour. The use of another machine learning approach, e.g. the SVMs and NNs as comparison, and extending the experiment with additional variables are suggested for future works.


8. ACKNOWLEDGMENT


We would like to express our gratitude to Miluše Tichavska, Academic Relation from marine traffic.com for providing the data and valuable feedback.


9. 1.


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©2015: The Royal Institution of Naval Architects


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