A comparative study of anomaly detection methods for gross error detection problems

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 (HBOS) is a fast unsupervised anomaly detection algorithm that has become more popular because of the rapid increase in the amount of data collected in recent decades. HBOS 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 HBOS named Adjusted HBOS (AHBOS), which considers neighboring bins prior to density estimation. Results from a simulation study and real data application indicate that AHBOS yields a better performance not only in the simulated data but also for various types of real data.

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A comparative study of anomaly detection methods for gross error detection problems

Cited by 3 publications

References 47 publications

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The Adjusted Histogram-Based Outlier Score - Ahbos

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