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 131–140 of 703 papers

Title	Date	Tasks	Status	Hype
PACOL: Poisoning Attacks Against Continual Learners	Nov 18, 2023	Continual LearningData Poisoning	—Unverified	0
SSB: Simple but Strong Baseline for Boosting Performance of Open-Set Semi-Supervised Learning	Nov 17, 2023	Multi-Task LearningOutlier Detection	CodeCode Available	0
Do Ensembling and Meta-Learning Improve Outlier Detection in Randomized Controlled Trials?	Nov 9, 2023	Meta-LearningModel Selection	CodeCode Available	0
IoT-Based Environmental Control System for Fish Farms with Sensor Integration and Machine Learning Decision Support	Nov 7, 2023	Feature EngineeringImputation	—Unverified	0
FOUND: Foot Optimization with Uncertain Normals for Surface Deformation Using Synthetic Data	Oct 27, 2023	Outlier DetectionSurface Normal Estimation	CodeCode Available	1
Concept-based Anomaly Detection in Retail Stores for Automatic Correction using Mobile Robots	Oct 21, 2023	Anomaly DetectionManagement	—Unverified	0
Outlier Detection Using Generative Models with Theoretical Performance Guarantees	Oct 16, 2023	Outlier Detection	—Unverified	0
MAGIC: Detecting Advanced Persistent Threats via Masked Graph Representation Learning	Oct 15, 2023	Graph Representation LearningOutlier Detection	CodeCode Available	1
Point Cloud Denoising and Outlier Detection with Local Geometric Structure by Dynamic Graph CNN	Oct 11, 2023	DenoisingOutlier Detection	—Unverified	0
Tight Rates in Supervised Outlier Transfer Learning	Oct 7, 2023	Outlier DetectionTransfer Learning	—Unverified	0

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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