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 201–210 of 703 papers

Title	Date	Tasks	Status
Concept-based Anomaly Detection in Retail Stores for Automatic Correction using Mobile Robots	Oct 21, 2023	Anomaly DetectionManagement	—Unverified
Outlier Detection Using Generative Models with Theoretical Performance Guarantees	Oct 16, 2023	Outlier Detection	—Unverified
Point Cloud Denoising and Outlier Detection with Local Geometric Structure by Dynamic Graph CNN	Oct 11, 2023	DenoisingOutlier Detection	—Unverified
Tight Rates in Supervised Outlier Transfer Learning	Oct 7, 2023	Outlier DetectionTransfer Learning	—Unverified
Data Cleaning and Machine Learning: A Systematic Literature Review	Oct 3, 2023	ImputationOutlier Detection	CodeCode Available
LS-VOS: Identifying Outliers in 3D Object Detections Using Latent Space Virtual Outlier Synthesis	Oct 2, 2023	3D Object DetectionAutonomous Driving	—Unverified
Understanding the Structure of QM7b and QM9 Quantum Mechanical Datasets Using Unsupervised Learning	Sep 25, 2023	DescriptiveOutlier Detection	—Unverified
Distribution and volume based scoring for Isolation Forests	Sep 20, 2023	Outlier Detection	CodeCode Available
Outlier-Insensitive Kalman Filtering: Theory and Applications	Sep 18, 2023	Outlier DetectionState Estimation	CodeCode Available
Boundary Peeling: Outlier Detection Method Using One-Class Peeling	Sep 11, 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