Rule-Based Error Detection and Correction to Operationalize Movement Trajectory Classification
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Classification of movement trajectories has many applications in transportation. Supervised neural models represent the current state-of-the-art. Recent security applications require this task to be rapidly employed in environments that may differ from the data used to train such models for which there is little training data. We provide a neuro-symbolic rule-based framework to conduct error correction and detection of these models to support eventual deployment in security applications. We provide a suite of experiments on several recent and state-of-the-art models and show an accuracy improvement of 1.7 present in training and when 40 obtain a 5.2 SOTA model without resorting to retraining of the base model.
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