Abstract
Traditionally, the performance of statistical tests for outlier detection is evaluated by their power and false alarm rate. It requires ensuring the upper bound for false alarm rate while measuring the detection power, which proves to be a difficult task. In this paper we introduce a new measure of outlier detection performance H m as the harmonic mean of the power and unit minus false alarm rate. The H m maximizes the detection power by minimizing the false alarm rate and enables an easier way for evaluation and parameters tuning of an outlier detection algorithm.
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Andrea, K., Shevlyakov, G., Vassilieva, N., Ulanov, A. (2014). A New Measure of Outlier Detection Performance. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2014. Lecture Notes in Computer Science(), vol 8556. Springer, Cham. https://doi.org/10.1007/978-3-319-08979-9_15
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DOI: https://doi.org/10.1007/978-3-319-08979-9_15
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-08978-2
Online ISBN: 978-3-319-08979-9
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