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On a Method to Measure Supervised Multiclass Model’s Interpretability: Application to Degradation Diagnosis (Short Paper)
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Citations
Cites
Survey and critique of techniques for extracting rules from trained artificial neural networks
Andrews, R., Diederich, J., & Tickle, A. B. (1995) · Knowledge-Based Systems · doi:10.1016/0950-7051(96)81920-4 · Cited by 1,225
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Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., et al. (2020) · Information Fusion · doi:10.1016/j.inffus.2019.12.012 · Cited by 10,423
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
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On the Opportunities and Risks of Foundation Models
Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., et al. (2021) · arXiv · doi:10.48550/arxiv.2108.07258 · Cited by 2,676
Metadata
- Authors
- Gauriat, C.-M., Pencolé, Y., Ribot, P., & Brouillet, G.
- Venue
- HAL (Le Centre pour la Communication Scientifique Directe)
- Publisher
- Schloss Dagstuhl – Leibniz-Zentrum für Informatik
- Published
- 2024
- OpenAlex
- W2962862931
- Cited by
- 12,586
- References
- 1
- FWCI
- 1941.79
- Type
- conference paper
- Language
- en
- Open access
- green
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- CoChat. (2026). Citation check: On a Method to Measure Supervised Multiclass Model’s Interpretability: Application to Degradation Diagnosis (Short Paper). https://app.cochat.ai/papers/10.4230/oasics.dx.2024.27
- The paper
- Gauriat, C.-M., Pencolé, Y., Ribot, P., & Brouillet, G. (2024). On a Method to Measure Supervised Multiclass Model’s Interpretability: Application to Degradation Diagnosis (Short Paper). HAL (Le Centre pour la Communication Scientifique Directe). https://doi.org/10.4230/oasics.dx.2024.27
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
