Histogram is a commonly used tool for visualizing data distribution. It has also been used in semi-supervised and unsupervised anomaly detection tasks. The histogram-based outlier score is a fast unsupervised anomaly detection method that has become more popular because of the rapid increase in the amount of data collected in recent decades. Histogram-based outlier score can be computed using either static or dynamic bin-width histograms. When a histogram contains large gaps, the dynamic bin-width approach is preferred over the static bin-width approach. These gaps in a histogram usually occur as a result of various distributions in real data. When working with a static bin-width histogram, gaps can be utilized to acquire better distinction between outliers and inliers. In this study, we propose an adjusted version of the histogram-based outlier score named adjusted histogram-based outlier score, which considers neighboring bins prior to density estimation. Results from a simulation study and real data application indicate that the adjusted histogram-based outlier score yields a better performance not only in the simulated data but also for various types of real data.
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Primary Language | English |
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Subjects | Engineering |
Journal Section | Articles |
Authors | |
Project Number | - |
Early Pub Date | June 28, 2023 |
Publication Date | June 30, 2023 |
Published in Issue | Year 2023 Volume: 9 Issue: 1 |
Mugla Journal of Science and Technology (MJST) is licensed under the Creative Commons Attribution-Noncommercial-Pseudonymity License 4.0 international license.