Histogram-based Outlier Score (HBOS): A fast Unsupervised Anomaly Detection Algorithm
By Markus Goldstein, Andreas R. Dengel. 2012
684 citations recorded by the source corpus.
View Paper: https://www.semanticscholar.org/paper/405bde43709582b0026c0fd6f0afe2c3c57f792e
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Sebastian Schmidl
- Phillip Wenig
- Zheng Li
- Thorsten Papenbrock
- Hongzhi Wang
- Mennatallah Amer
- Mohamed Jaward Bah
- Nicola Botta
Citations in the corpus
- Enhancing one-class support vector machines for unsupervised anomaly detection (2013, methodology): “The Histogram-based Outlier Score (HBOS) is an unsupervised statistical based approach that was suggested in [10].”
- Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges (2017, background): “Local density cluster based outlier factor [158], clustering basedmultivariate Gaussian outlier score [159], [160] and histogram based outlier score [161] are the famous cluster based outlier detection models in literature.”
- ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions (2022, methodology): “Instead, one could capture more information of the distribution by building a histogram of the data and use bins with low counts to determine where outliers are [30].”
- PyOD: A Python Toolbox for Scalable Outlier Detection (2019, background): “, 2008) Ensembling No Yes HBOS (Goldstein and Dengel, 2012) Proximity Yes No SOS (Janssens et al.”
- Anomaly Detection in Time Series: A Comprehensive Evaluation (2022, methodology): “…Classic ML PCA[121] NetworkSVM[160] MS-SVDD[149] sequenceMiner[23] NoveltySVR [86] k-Means [151] RandomForestRegressor [165] SLADE-MTS[142] HBOS [47] PCC [121] HybridK-Means[140] RandomBlackForest [165] PhaseSpace-SVM [85] KNN [110] SLADE-TS[141] RobustPCA [101] S-SVM[11] XGBoosting [34]…”
- Progress in Outlier Detection Techniques: A Survey (2019, methodology): “Many statistical approaches have been proposed, but among the more straightforward statistical methods to identify outliers are the histogram [116] and other statistical tests [40] such as the Boxplot, Trimmed mean, Extreme Studentized Deviate and the Dixon-type test [40].”
- DeepAnT: A Deep Learning Approach for Unsupervised Anomaly Detection in Time Series (2019, background): “Histogram-Based Outlier Score (HBOS) [16] is another statistical unsupervised anomaly detection algorithm.”