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 291–300 of 703 papers

Title	Date	Tasks	Status
Find the word that does not belong: A Framework for an Intrinsic Evaluation of Word Vector Representations	Aug 1, 2016	Machine TranslationOutlier Detection	—Unverified
Fin-Fed-OD: Federated Outlier Detection on Financial Tabular Data	Apr 23, 2024	Anomaly DetectionFederated Learning	—Unverified
Outlier detection using flexible categorisation and interrogative agendas	Dec 19, 2023	Meta-LearningOutlier Detection	—Unverified
Flexible categorization using formal concept analysis and Dempster-Shafer theory	Aug 23, 2024	Outlier Detection	—Unverified
FlexUOD: The Answer to Real-world Unsupervised Image Outlier Detection	Jan 1, 2025	Outlier Detection	—Unverified
OneFlow: One-class flow for anomaly detection based on a minimal volume region	Oct 6, 2020	Anomaly DetectionOne-class classifier	—Unverified
Class Imbalance in Anomaly Detection: Learning from an Exactly Solvable Model	Jan 20, 2025	Anomaly DetectionOutlier Detection	—Unverified
Can Dense Connectivity Benefit Outlier Detection? An Odyssey with NAS	Jun 4, 2024	Neural Architecture SearchOutlier Detection	—Unverified
Holistic Features For Real-Time Crowd Behaviour Anomaly Detection	Jun 16, 2016	Anomaly DetectionBinary Classification	—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