Abstract
The usual formulas for gauging the quality of a classification method assume that we know the ground truth, i.e., that for several objects, we know for sure to which class they belong. In practice, we often only know this with some degree of certainty. In this paper, we explain how to take this uncertainty into account when gauging the quality of a classification method.
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References
Gray, N., Ferson, S., Kreinovich, V.: How to gauge the quality of a testing method when ground truth is known with uncertainty. In: Proceedings of the 9th International Workshop on Reliable Engineering Computing REC’2021, Taormina, Italy, May 16–20, 2021, pp. 265–278 (2021)
Sheskin, D.J.: Handbook of Parametric and Non-Parametric Statistical Procedures. Chapman & Hall/CRC, London, UK (2011)
Acknowledgements
This work was supported in part by the National Science Foundation grants:
\(\bullet \) 1623190 (A Model of Change for Preparing a New Generation for Professional Practice in Computer Science), and
\(\bullet \) HRD-1834620 and HRD-2034030 (CAHSI Includes).
It was also supported:
\(\bullet \) by the AT &T Fellowship in Information Technology, and
\(\bullet \) by the program of the development of the Scientific-Educational Mathematical Center of Volga Federal District No. 075-02-2020-1478.
The authors are thankful to all the participants of the 26th Annual UTEP/NMSU Workshop on Mathematics, Computer Science, and Computational Science (El Paso, Texas, November 5, 2021) for valuable discussions.
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Mendez, R., Osaretin, O., Kreinovich, V. (2023). How to Gauge the Quality of a Multi-class Classification When Ground Truth Is Known with Uncertainty. In: Ceberio, M., Kreinovich, V. (eds) Decision Making Under Uncertainty and Constraints. Studies in Systems, Decision and Control, vol 217. Springer, Cham. https://doi.org/10.1007/978-3-031-16415-6_19
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DOI: https://doi.org/10.1007/978-3-031-16415-6_19
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