Abstract
We argue that in many practical situations control of False Omission Rate (FOR) or Bayesian False Omission Rate (BFOR) is of primary importance. We develop and investigate such rule in the context of outlier detection, and propose its empirical formulation for practical use. We consider several score statistics used to detect outliers and study how well the introduced method controls FOR in practice. It is shown by analysis of several datasets that FOR control in contrast to FDR control is inherently tied to performance of the score statistic employed on both inlier and outlier data sets.
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Wawrzeńczyk, A., Mielniczuk, J. (2023). Outlier Detection Under False Omission Rate Control. In: Mikyška, J., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M. (eds) Computational Science – ICCS 2023. ICCS 2023. Lecture Notes in Computer Science, vol 10475. Springer, Cham. https://doi.org/10.1007/978-3-031-36024-4_47
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DOI: https://doi.org/10.1007/978-3-031-36024-4_47
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