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 301–310 of 703 papers

Title	Date	Tasks	Status
Weighted Scaling Approach for Metabolomics Data Analysis	Aug 1, 2022	Outlier Detection	CodeCode Available
3D Labeling Tool	Jul 23, 2022	Objectobject-detection	—Unverified
An Evolutionary Game based Secure Clustering Protocol with Fuzzy Trust Evaluation and Outlier Detection for Wireless Sensor Networks	Jul 21, 2022	ClusteringOutlier Detection	—Unverified
A Secure Clustering Protocol with Fuzzy Trust Evaluation and Outlier Detection for Industrial Wireless Sensor Networks	Jul 20, 2022	ClusteringOutlier Detection	—Unverified
Outlier Explanation via Sum-Product Networks	Jul 18, 2022	Decision MakingOutlier Detection	—Unverified
Repairing Systematic Outliers by Learning Clean Subspaces in VAEs	Jul 17, 2022	Outlier Detection	CodeCode Available
Suppressing Poisoning Attacks on Federated Learning for Medical Imaging	Jul 15, 2022	Federated LearningOutlier Detection	CodeCode Available
Outlier detection of vital sign trajectories from COVID-19 patients	Jul 15, 2022	Outlier DetectionTime Series	CodeCode Available
A geometric framework for outlier detection in high-dimensional data	Jul 1, 2022	Anomaly DetectionOutlier Detection	—Unverified
K-ARMA Models for Clustering Time Series Data	Jun 30, 2022	ClusteringOutlier 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