Evaluation of Interpretable Association Rule Mining Methods on Time-Series in the Maritime Domain
In decision critical domains, the results generated by black box models such as state of the art deep learning based classifiers raise questions regarding their explainability. In order to ensure the trust of operators in these systems, an explanation of the reasons behind the predictions is crucial. As rule-based approaches rely on simple if-then statements which can easily be understood by a human operator they are considered as an interpretable prediction model. Therefore, association rule mining methods are applied for explaining time-series classifier in the maritime domain. Three rule mining algorithms are evaluated on the classification of vessel types trained on a real world dataset. Each one is a surrogate model which mimics the behavior of the underlying neural network. In the experiments the GiniReg method performs the best, resulting in a less complex model which is easier to interpret. The SBRL method works well in terms of classification performance but due to an increase in complexity, it is more challenging to explain. Furthermore, during the evaluation the impact of hyper-parameters on the performance of the model along with the execution time of all three approaches is analyzed.