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 161–170 of 703 papers

Title	Date	Tasks	Status
Comparative Study of Neighbor-based Methods for Local Outlier Detection	May 29, 2024	Outlier Detection	—Unverified
Comparison of Outlier Detection Techniques for Structured Data	Jun 16, 2021	BIG-bench Machine LearningOutlier Detection	—Unverified
Comparison of Visual Trackers for Biomechanical Analysis of Running	May 7, 2025	Outlier DetectionPose Estimation	—Unverified
Capturing the Denoising Effect of PCA via Compression Ratio	Apr 22, 2022	ClusteringDenoising	—Unverified
AI-enabled Blockchain: An Outlier-aware Consensus Protocol for Blockchain-based IoT Networks	Jun 17, 2019	BIG-bench Machine LearningOutlier Detection	—Unverified
Concept-based Anomaly Detection in Retail Stores for Automatic Correction using Mobile Robots	Oct 21, 2023	Anomaly DetectionManagement	—Unverified
Concept Learning through Deep Reinforcement Learning with Memory-Augmented Neural Networks	Nov 15, 2018	Deep Reinforcement LearningOne-Shot Learning	—Unverified
Conditional Selective Inference for Robust Regression and Outlier Detection using Piecewise-Linear Homotopy Continuation	Apr 22, 2021	Outlier Detectionregression	—Unverified
Conditional Testing based on Localized Conformal p-values	Sep 25, 2024	Outlier DetectionPrediction Intervals	—Unverified
Cognitive Deep Machine Can Train Itself	Dec 2, 2016	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