{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/rule-based-error-detection-and-correction-to","title":"Rule-Based Error Detection and Correction to Operationalize Movement Trajectory Classification","arxiv_id":"2308.14250","date":"2023-08-28","proceeding":null,"authors":["Bowen Xi","Kevin Scaria","Divyagna Bavikadi","Paulo Shakarian"],"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.","url_abs":"https://arxiv.org/abs/2308.14250v4","url_pdf":"https://arxiv.org/pdf/2308.14250v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"rule-based-error-detection-and-correction-to","repo_url":"https://github.com/lab-v2/error-detection-and-correction","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"rule-based-error-detection-and-correction-to","repo_url":"https://github.com/lab-v2/PyEDCR","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"anomaly-detection","task_name":"Anomaly Detection"}],"methods":[{"method_slug":"base","method_name":"BASE"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2308.14250","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}