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 261–270 of 703 papers

Title	Date	Tasks	Status
A New Approach To Two-View Motion Segmentation Using Global Dimension Minimization	Apr 10, 2013	ClusteringMotion Segmentation	—Unverified
A Framework for Clustering Uncertain Data	Aug 1, 2015	ClusteringOutlier Detection	—Unverified
Distance Based Pattern Driven Mining for Outlier Detection in High Dimensional Big Dataset	Oct 5, 2021	Outlier Detection	—Unverified
Distance approximation using Isolation Forests	Oct 27, 2019	Missing ValuesOutlier Detection	—Unverified
Discovering outliers in the Mars Express thermal power consumption patterns	Aug 4, 2021	Outlier Detection	—Unverified
Component-wise Adaptive Trimming For Robust Mixture Regression	May 23, 2020	Outlier Detectionparameter estimation	—Unverified
Diffusion Nets	Jun 25, 2015	DecoderOutlier Detection	—Unverified
Differential Privacy for Anomaly Detection: Analyzing the Trade-off Between Privacy and Explainability	Apr 9, 2024	Anomaly DetectionOutlier Detection	—Unverified
Automated detection of business-relevant outliers in e-commerce conversion rate	May 15, 2019	Outlier Detection	—Unverified
Differentially Private Analysis of Outliers	Jul 24, 2015	Outlier DetectionSensitivity	—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