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

• URL: http://arxiv.org/abs/2308.14250v3
• Date: Fri, 2 Aug 2024 01:38:16 GMT
• Title: Rule-Based Error Detection and Correction to Operationalize Movement Trajectory Classification
• Authors: Bowen Xi, Kevin Scaria, Divyagna Bavikadi, Paulo Shakarian,
• Abstract summary: We provide a neuro-symbolic rule-based framework to conduct error correction and detection of models to integrate into our movement trajectory platform. We show an F1 scores for predicting errors of up to 0.984, significant performance increase for out-of distribution accuracy (8.51% improvement over SOTA for zero-shot accuracy) and accuracy improvement over the SOTA model.
• Score: 1.192247515575942
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Classification of movement trajectories has many applications in transportation and is a key component for large-scale movement trajectory generation and anomaly detection which has key safety applications in the aftermath of a disaster or other external shock. However, the current state-of-the-art (SOTA) are based on supervised deep learning - which leads to challenges when the distribution of trajectories changes due to such a shock. We provide a neuro-symbolic rule-based framework to conduct error correction and detection of these models to integrate into our movement trajectory platform. We provide a suite of experiments on several recent SOTA models where we show highly accurate error detection, the ability to improve accuracy with a changing test distribution, and accuracy improvement for the base use case in addition to a suite of theoretical properties that informed algorithm development. Specifically, we show an F1 scores for predicting errors of up to 0.984, significant performance increase for out-of distribution accuracy (8.51% improvement over SOTA for zero-shot accuracy), and accuracy improvement over the SOTA model.

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