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
Data refinement for fully unsupervised visual inspection using pre-trained networks	Feb 25, 2022	Anomaly DetectionOne-Class Classification	—Unverified
Choquet-Based Fuzzy Rough Sets	Feb 22, 2022	BIG-bench Machine LearningOutlier Detection	—Unverified
Backdoor Defense in Federated Learning Using Differential Testing and Outlier Detection	Feb 21, 2022	backdoor defenseFederated Learning	—Unverified
Outlier-based Autism Detection using Longitudinal Structural MRI	Feb 21, 2022	Autism detectionGenerative Adversarial Network	—Unverified
Hybridization of Capsule and LSTM Networks for unsupervised anomaly detection on multivariate data	Feb 11, 2022	Anomaly DetectionChange Point Detection	—Unverified
Geometry- and Accuracy-Preserving Random Forest Proximities	Jan 29, 2022	Data VisualizationImputation	CodeCode Available
EVBattery: A Large-Scale Electric Vehicle Dataset for Battery Health and Capacity Estimation	Jan 28, 2022	Anomaly DetectionCapacity Estimation	—Unverified
A deep mixture density network for outlier-corrected interpolation of crowd-sourced weather data	Jan 25, 2022	Outlier Detection	—Unverified
Adaptive Outlier Detection for Power MOSFETs Based on Gaussian Process Regression	Jan 25, 2022	GPROutlier Detection	—Unverified
Community-based anomaly detection using spectral graph filtering	Jan 24, 2022	Anomaly DetectionOutlier 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