Slim, Hussein et Nadeau, Sylvie.
2021.
« The application of Rough Sets Theory as a data-mining tool to classify complex functions in safety management ».
In Frühjahrskongress der Gesellschaft für Arbeitswissenschaft (Bochum, Germany, Mar. 03-05, 2021)
Coll. « Kongress der Gesellschaft für Arbeitswissenschaft », vol. 67.
Dortmund : GfA-Press.
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Résumé
In recent years, considerable research efforts in safety manage-ment were directed at proposing innovative methodological frameworks to address the complexity of modern sociotechnical systems. The significance of results in such endeavors, whether quantitative or qualitative, relies largely on the quality of input data and the validity of the implemented meth-ods to model such systems. To provide more objective and valid results, new protocols and tools for data processing are needed as well. An inter-esting data-mining tool for computing with incomplete and uncertain infor-mation is Rough Set Theory (RST). In this study, we propose the application of RST to generate comprehensible IF-THEN rule bases for classifying out-comes within the framework of the Functional Resonance Analysis Method (FRAM). The steps for the integration process of both frameworks are intro-duced in this paper and an illustrative example is consequently provided to demonstrate a possible approach for realizing the combination. Such an ap-proach could allow for an efficient rule generation and data classification process, which could aid in addressing classification challenges and input data limitations in safety management. The model however still requires fur-ther optimization and validation using expert’s input data in future applica-tions.
| Type de document: | Compte rendu de conférence |
|---|---|
| ISBN: | 978-3-936804-29-4 |
| Informations complémentaires: | Arbeit HUMAINE gestalten; Identifiant de l'article: Paper B.12.9 |
| Professeur: | Professeur Nadeau, Sylvie |
| Affiliation: | Génie mécanique |
| Date de dépôt: | 05 mars 2021 19:12 |
| Dernière modification: | 12 mars 2021 20:18 |
| URI: | https://espace2.etsmtl.ca/id/eprint/22349 |
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