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 271–280 of 703 papers

Title	Date	Tasks	Status
Cascade Subspace Clustering for Outlier Detection	Jun 23, 2023	ClusteringOutlier Detection	—Unverified
Positive Difference Distribution for Image Outlier Detection using Normalizing Flows and Contrastive Data	Aug 30, 2022	Anomaly DetectionOutlier Detection	—Unverified
Can we predict QPP? An approach based on multivariate outliers	Feb 7, 2024	Outlier Detection	—Unverified
Fairness-aware Outlier Ensemble	Mar 17, 2021	FairnessFraud Detection	—Unverified
Anomaly Detection using Capsule Networks for High-dimensional Datasets	Dec 27, 2021	Anomaly DetectionBinary Classification	—Unverified
A Hybrid Deep Feature-Based Deformable Image Registration Method for Pathology Images	Aug 16, 2022	Image RegistrationOutlier Detection	—Unverified
Fin-Fed-OD: Federated Outlier Detection on Financial Tabular Data	Apr 23, 2024	Anomaly DetectionFederated Learning	—Unverified
Extending Decision Predicate Graphs for Comprehensive Explanation of Isolation Forest	May 6, 2025	Outlier Detection	—Unverified
Can Dense Connectivity Benefit Outlier Detection? An Odyssey with NAS	Jun 4, 2024	Neural Architecture SearchOutlier Detection	—Unverified
Exploring Outliers in Crowdsourced Ranking for QoE	Jul 18, 2017	Outlier Detection	—Unverified

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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