THE ADJUSTED HISTOGRAM-BASED OUTLIER SCORE - AHBOS

Authors : Uğur Binzat, Engin Yildiztepe

Pages : 92-100

Doi:10.22531/muglajsci.1252876

View : 29 | Download : 27

Publication Date : 2023-06-30

Article Type : Research Article

Abstract :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.
Keywords : unsupervised anomaly detection, outlier, histogram, density estimation