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 211–220 of 703 papers

Title	Date	Tasks	Status
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
Anomaly-Injected Deep Support Vector Data Description for Text Outlier Detection	Oct 27, 2021	Anomaly DetectionOutlier Detection	—Unverified
Closed-Form, Provable, and Robust PCA via Leverage Statistics and Innovation Search	Jun 23, 2021	ClusteringForm	—Unverified
Detection of Abnormal Input-Output Associations	Aug 3, 2017	Outlier DetectionRelation	—Unverified
A Unified Framework for Center-based Clustering of Distributed Data	Feb 2, 2024	ClusteringOutlier Detection	—Unverified
Detection of Peculiar Word Sense by Distance Metric Learning with Labeled Examples	May 1, 2012	General ClassificationMetric Learning	—Unverified
Adaptive Outlier Detection for Power MOSFETs Based on Gaussian Process Regression	Jan 25, 2022	GPROutlier 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