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 621–630 of 703 papers

Title	Date	Tasks	Status
Dense outlier detection and open-set recognition based on training with noisy negative images	Jan 22, 2021	Open Set LearningOutlier Detection	—Unverified
Detecting abnormal events in video using Narrowed Normality Clusters	Jan 12, 2018	Anomaly DetectionClustering	—Unverified
EVBattery: A Large-Scale Electric Vehicle Dataset for Battery Health and Capacity Estimation	Jan 28, 2022	Anomaly DetectionCapacity Estimation	—Unverified
Detecting outliers by clustering algorithms	Dec 7, 2024	ClusteringOutlier Detection	—Unverified
Detecting Outliers in Data with Correlated Measures	Aug 26, 2018	Outlier Detection	—Unverified
Detecting Point Outliers Using Prune-based Outlier Factor (PLOF)	Nov 5, 2019	Anomaly DetectionOutlier Detection	—Unverified
Detecting Surprising Situations in Event Data	Aug 29, 2022	Outlier Detection	—Unverified
Detecting Unusual Input-Output Associations in Multivariate Conditional Data	Dec 21, 2016	Outlier Detection	—Unverified
Detection of Abnormal Input-Output Associations	Aug 3, 2017	Outlier DetectionRelation	—Unverified
Detection of Peculiar Word Sense by Distance Metric Learning with Labeled Examples	May 1, 2012	General ClassificationMetric Learning	—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