Abstract
Noise resilience is a popular attribute among machine learning algorithms. In regression problems, it refers to the ability to keep high performance even when the data is noisy. Surprisingly, there is no standard figure of merit to quantify it. This theoretical research leverages the variance of the residuals to determine objectively the performance of a regression algorithm in the presence of noisy data. The two main contributions of the paper are the locality conditions of noise resilience and the noise resilience score, NR.
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Acknowledgements
The project that generated these results was supported by a grant from the “la Caixa” Banking Foundation (ID 100010434), whose code is LCF/BQ/AA19/11720045.
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Viaña, J., Cohen, K. (2022). Evaluation Criteria for Noise Resilience in Regression Algorithms. In: Rayz, J., Raskin, V., Dick, S., Kreinovich, V. (eds) Explainable AI and Other Applications of Fuzzy Techniques. NAFIPS 2021. Lecture Notes in Networks and Systems, vol 258. Springer, Cham. https://doi.org/10.1007/978-3-030-82099-2_43
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