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Journals / Mugla Journal of Science and Technology / 2026 / Volume: 9 Issue: 1

THE ADJUSTED HISTOGRAM-BASED OUTLIER SCORE - AHBOS

Uğur Binzat(DOKUZ EYLÜL ÜNİVERSİTESİ)0000-0003-1387-6661
Engin Yıldıztepe(DOKUZ EYLÜL ÜNİVERSİTESİ)0000-0002-7617-4934
Pages
—
Publication Date
2023-06-30
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

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.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Histogram is a commonly used tool for visualizing data distribution.
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Key Findings & Data: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.
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Conclusion & Impact: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.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:unsupervised anomaly detection · outlier · histogram

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.

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Cite This Article

Binzat, U., Yıldıztepe, E. (2023). THE ADJUSTED HISTOGRAM-BASED OUTLIER SCORE - AHBOS. Mugla Journal of Science and Technology, Volume: 9 Issue: 1. https://doi.org/10.22531/muglajsci.1252876

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