Rule-Based Error Detection and Correction to Operationalize Movement
  Trajectory Classification
Bowen Xi Kevin Scaria Paulo Shakarian
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% over the SOTA model in the case where all classes are
present in training and when 40% of classes are omitted from training, we
obtain a 5.2% improvement (zero-shot) and 23.9% (few-shot) improvement over the
SOTA model without resorting to retraining of the base model.