Abstract
Machine Learning and Deep Learning models make accurate predictions based on a specifically trained task. For instance, models that classify ship vessel types based on their trajectory and other features. This can support human experts while they try to obtain information on the ships, e.g., to control illegal fishing. Besides the support in predicting a certain ship type, there is a need to explain the decision-making behind the classification. For example, which features contributed the most to the classification of the ship type. This paper introduces existing explanation approaches to the task of ship classification. The underlying model is based on a Residual Neural Network. The model was trained on an AIS data set. Further, we illustrate the explainability approaches by means of an explanatory case study and conduct a first experiment with a human expert.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Gundogdu, E., Solmaz, B., Ycesoy, V., Koç, A.: Marvel: A large-scale image dataset for maritime vessels. In: Lai, S.H., Lepetit, V., Nishino, K., Sato, Y. (eds.) Asian Conference on Computer Vision, pp. 165–180. Springer, Cham (2016)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Anneken, M., Strenger, M., Robert, S., Beyerer J.: Classification of Maritime Vessels using Convolutional Neural Networks. UR-AI 2020, accepted for publication (2020)
Tetreault, B.J.: Use of the Automatic Identification System (AIS) for maritime domain awareness (MDA). In: Proceedings of OCEANS 2005 MTS/IEEE, pp. 1590–1594. IEEE, September 2005
Doshi-Velez, F., Kim, B.: Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608) (2017
Denadai, E.P.: Model Interpretability of Deep Neural Networks (2020). http://www.ncbi.nlm.nih.gov
Shapley, L.S.: A value for n-person games. Contrib. Theory Games 2(28), 307–317 (1953)
Lundberg, S.M., Lee, S.I.: A unified approach to interpreting model predictions. In: Advances in Neural Information Processing Systems, pp. 4765–4774 (2017)
Molnar, C.: Interpretable machine learning. Lulu.com (2019)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Fisher, A., Rudin, C., Dominici, F.: Model class reliance: variable importance measures for any machine learning model class, from the “rashomon" perspective. arXiv preprint arXiv:1801.01489, p. 68 (2018)
Ribeiro, M. T., Singh, S., Guestrin, C.: “Why should i trust you?” explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135–1144 (2016)
Poursabzi-Sangdeh, F., Goldstein, D.G., Hofman, J.M., Vaughan, J.W., Wallach, H.: Manipulating and measuring model interpretability. arXiv preprint arXiv:1802.07810 (2018)
Lage, I., Chen, E., He, J., Narayanan, M., Kim, B., Gershman, S., Doshi-Velez, F.: An evaluation of the human-interpretability of explanation. arXiv preprint arXiv:1902.00006 (2019)
Schmidt, P., Biessmann, F.: Quantifying interpretability and trust in machine learning systems. arXiv preprint arXiv:1901.08558 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Burkart, N., Huber, M.F., Anneken, M. (2021). Supported Decision-Making by Explainable Predictions of Ship Trajectories. In: Herrero, Á., Cambra, C., Urda, D., Sedano, J., Quintián, H., Corchado, E. (eds) 15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020). SOCO 2020. Advances in Intelligent Systems and Computing, vol 1268. Springer, Cham. https://doi.org/10.1007/978-3-030-57802-2_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-57802-2_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-57801-5
Online ISBN: 978-3-030-57802-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)