Outlier Detection

Outlier Detection is a task of identifying a subset of a given data set which are considered anomalous in that they are unusual from other instances. It is one of the core data mining tasks and is central to many applications. In the security field, it can be used to identify potentially threatening users, in the manufacturing field it can be used to identify parts that are likely to fail.

Source: Coverage-based Outlier Explanation

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 51–60 of 703 papers

Title	Date	Tasks	Status	Hype	Score
Testing for Outliers with Conformal p-values	Apr 16, 2021	Outlier Detectionvalid	CodeCode Available	1	5
NEAR - Newborns EEG Artifact Removal	Jan 15, 2022	EEGEEG Artifact Removal	CodeCode Available	1	5
On Using Classification Datasets to Evaluate Graph-Level Outlier Detection: Peculiar Observations and New Insights	Dec 23, 2020	Binary ClassificationGeneral Classification	CodeCode Available	1	5
Clustered Hierarchical Anomaly and Outlier Detection Algorithms	Feb 9, 2021	Anomaly DetectionClustering	CodeCode Available	1	5
Coniferest: a complete active anomaly detection framework	Oct 22, 2024	Anomaly DetectionOutlier Detection	CodeCode Available	1	5
COPOD: Copula-Based Outlier Detection	Sep 20, 2020	Outlier Detection	CodeCode Available	1	5
Outlier detection by ensembling uncertainty with negative objectness	Feb 23, 2024	Outlier DetectionPrediction	CodeCode Available	1	5
Outlier detection in multivariate functional data through a contaminated mixture model	Jun 14, 2021	Outlier Detection	CodeCode Available	1	5
ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions	Jan 2, 2022	Anomaly DetectionOutlier Detection	CodeCode Available	1	5
FOUND: Foot Optimization with Uncertain Normals for Surface Deformation Using Synthetic Data	Oct 27, 2023	Outlier DetectionSurface Normal Estimation	CodeCode Available	1	5

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All datasets ECG5000 Balance scale_class 1 Breast cancer Wisconsin_class 2 Breast cancer Wisconsin_class 4 Fashion-MNIST Glass identification Heart-C Hepatitis Internet Ad Ionosphere_class b SKAB

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	VRAE+SVM	Accuracy	0.98	—	Unverified
2	F-t ALSTM-FCN	Accuracy	0.95	—	Unverified
3	GENDIS	Accuracy	0.94	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ASVDD	Average Accuracy	99.03	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ASVDD	Average Accuracy	37.62	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ASVDD	Average Accuracy	65.6	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	PAE	AUROC	1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ASVDD	Average Accuracy	99.05	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MIX	AUC	0.86	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MIX	AUC-ROC	0.85	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MIX	AUC-ROC	0.93	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ASVDD	Average Accuracy	86.33	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	LSTMCaps	Average F1	0.74	—	Unverified