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On Heterogeneous Ensembles for Anomaly Detection: Ask Three Experts

https://doi.org/10.2139/ssrn.6408265
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Index for rating diagnostic tests
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Ensemble learning: A survey
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That Elusive Diversity in Classifier Ensembles
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Optimal Thresholding of Classifiers to Maximize F1 Measure
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Local outlier detection reconsidered: a generalized view on locality with applications to spatial, video, and network outlier detection
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On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study
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On the evaluation of outlier detection and one-class classification: a comparative study of algorithms, model selection, and ensembles
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Evaluating outlier probabilities: assessing sharpness, refinement, and calibration using stratified and weighted measures
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Loda: Lightweight on-line detector of anomalies
inferredcites →https://doi.org/10.1007/s41060-021-00265-1openalex-snapshot
On the nature and types of anomalies: a review of deviations in data
inferredcites →https://doi.org/10.1109/fuzz62266.2025.11197685openalex-snapshot
Interpreting and Unifying Anomaly Scores with Antagonistic Fuzzy Sets
inferredcites →https://doi.org/10.1109/icdm.2008.17openalex-snapshot
Isolation Forest
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Stability Selection
inferredcites →https://doi.org/10.1137/1.9781611972818.2openalex-snapshot
Interpreting and Unifying Outlier Scores
inferredcites →https://doi.org/10.1137/1.9781611972825.90openalex-snapshot
On Evaluation of Outlier Rankings and Outlier Scores
inferredcites →https://doi.org/10.1145/1081870.1081891openalex-snapshot
Feature bagging for outlier detection
inferredcites →https://doi.org/10.1145/1401890.1401946openalex-snapshot
Angle-based outlier detection in high-dimensional data
inferredcites →https://doi.org/10.1145/2487575.2487676openalex-snapshot
Subsampling for efficient and effective unsupervised outlier detection ensembles
inferredcites →https://doi.org/10.1145/2689746.2689747openalex-snapshot
Anomaly Detection Using Autoencoders with Nonlinear Dimensionality Reduction
inferredcites →https://doi.org/10.1145/2890508openalex-snapshot
Less is More
inferredcites →https://doi.org/10.1145/335191.335437openalex-snapshot
Efficient algorithms for mining outliers from large data sets
inferredcites →https://doi.org/10.1145/342009.335388openalex-snapshot
LOF
inferredcites →https://doi.org/10.1148/radiology.143.1.7063747openalex-snapshot
The meaning and use of the area under a receiver operating characteristic (ROC) curve.
inferredcites →https://doi.org/10.1162/089976601750264965openalex-snapshot
Estimating the Support of a High-Dimensional Distribution
inferredcites →https://doi.org/10.1186/s12864-019-6413-7openalex-snapshot
The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation
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The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets
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