THE ADJUSTED HISTOGRAM-BASED OUTLIER SCORE - AHBOS

Yıl 2023, Cilt: 9 Sayı: 1, 92 - 100, 30.06.2023

Uğur Binzat , Engin Yıldıztepe

https://doi.org/10.22531/muglajsci.1252876

Öz

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.

Anahtar Kelimeler

unsupervised anomaly detection, outlier, histogram, density estimation

Destekleyen Kurum

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Proje Numarası

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Teşekkür

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Kaynakça

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