Rule-Based Error Detection and Correction to Operationalize Movement Trajectory Classification

arXiv:2308.14250v2 Announce Type: replace
Abstract: 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 …

abstract applications art arxiv classification cs.ai cs.lg cs.lo current data detection environments error security state train training trajectory transportation type