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An Interpretable Aid Decision-Making Model for Flag State Control Ship Detention Based on SMOTE and XGBoost
2021
Journal of Marine Science and Engineering
The reasonable decision of ship detention plays a vital role in flag state control (FSC). Machine learning algorithms can be applied as aid tools for identifying ship detention. In this study, we propose a novel interpretable ship detention decision-making model based on machine learning, termed SMOTE-XGBoost-Ship detention model (SMO-XGB-SD), using the extreme gradient boosting (XGBoost) algorithm and the synthetic minority oversampling technique (SMOTE) algorithm to identify whether a ship
doi:10.3390/jmse9020156
fatcat:fc7pl7lvhvgcngptsqa2i2hrnq