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Semantic Scholar · Article (Proceedings of the Human Factors and Ergonomics Society Annual Meeting)
A Comparison of Rule-Based and Machine Learning Models for Classification of Human Factors Aviation Safety Event Reports
Attribution
This is the abstract and citation. Full text lives at Semantic Scholar — we link out rather than host. All credit to the authors and Proceedings of the Human Factors and Ergonomics Society Annual Meeting.
Abstract
Verbatim from Semantic Scholar. Not paraphrased, not summarized.
There is growing interest in the study and practice of applying data science (DS) and machine learning (ML) to automate decision making in safety-critical industries. As an alternative or augmentation to human review, there are opportunities to explore these methods for classifying aviation operational events by root cause. This study seeks to apply a thoughtful approach to design, compare, and combine rule-based and ML techniques to classify events caused by human error in aircraft/engine assembly, maintenance or operation. Event reports contain a combination of continuous parameters, unstructured text entries, and categorical selections. A Human Factors approach to classifier development prioritizes the evaluation of distinct data features and entry methods to improve modeling. Findings, including the performance of tested models, led to recommendations for the design of textual data collection systems and classification approaches.
Authors
- Katherine Darveau
- D. Hannon
- Chad Foster
Keywords
- Engineering
- Computer Science
Citation: Katherine Darveau, D. Hannon, Chad Foster (2020). A Comparison of Rule-Based and Machine Learning Models for Classification of Human Factors Aviation Safety Event Reports. Proceedings of the Human Factors and Ergonomics Society Annual Meeting. Semantic Scholar ID 0295ddb26d6e30bcf100da2670a7fb55f72d6a34. https://doi.org/10.1177/1071181320641034 ↗