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 571–580 of 703 papers

Title	Date	Tasks	Status
Brain Ageing Prediction using Isolation Forest Technique and Residual Neural Network (ResNet)	Dec 26, 2024	Age EstimationOutlier Detection	—Unverified
Breakdown Point of Robust Support Vector Machine	Sep 3, 2014	Outlier DetectionSensitivity	—Unverified
Byzantine-Resilient Secure Federated Learning	Jul 21, 2020	Federated LearningOutlier Detection	—Unverified
ByzSecAgg: A Byzantine-Resistant Secure Aggregation Scheme for Federated Learning Based on Coded Computing and Vector Commitment	Feb 20, 2023	Federated LearningOutlier Detection	—Unverified
C-AllOut: Catching & Calling Outliers by Type	Oct 13, 2021	Outlier DetectionTAG	—Unverified
Can Dense Connectivity Benefit Outlier Detection? An Odyssey with NAS	Jun 4, 2024	Neural Architecture SearchOutlier Detection	—Unverified
Can we predict QPP? An approach based on multivariate outliers	Feb 7, 2024	Outlier Detection	—Unverified
Cascade Subspace Clustering for Outlier Detection	Jun 23, 2023	ClusteringOutlier Detection	—Unverified
Cascade Watchdog: A Multi-tiered Adversarial Guard for Outlier Detection	Aug 20, 2021	Outlier DetectionOut-of-Distribution Detection	—Unverified
Centering the Margins: Outlier-Based Identification of Harmed Populations in Toxicity Detection	May 24, 2023	Outlier 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