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 441–450 of 703 papers

Title	Date	Tasks	Status
Traffic congestion anomaly detection and prediction using deep learning	Jun 23, 2020	Anomaly DetectionDeep Learning	—Unverified
Transfer Neyman-Pearson Algorithm for Outlier Detection	Jan 2, 2025	Classificationimbalanced classification	—Unverified
Transformation Autoregressive Networks	Jan 30, 2018	Density EstimationOutlier Detection	—Unverified
TRIDIS: A Comprehensive Medieval and Early Modern Corpus for HTR and NER	Mar 25, 2025	Handwritten Text RecognitionHTR	—Unverified
Trojan Attacks on Wireless Signal Classification with Adversarial Machine Learning	Oct 23, 2019	BIG-bench Machine LearningClassification	—Unverified
tsrobprep - an R package for robust preprocessing of time series data	Apr 26, 2021	ClusteringImputation	—Unverified
Two-phase Dual COPOD Method for Anomaly Detection in Industrial Control System	Apr 30, 2023	Anomaly DetectionOutlier Detection	—Unverified
Uncertainty in Supply Chain Digital Twins: A Quantum-Classical Hybrid Approach	Nov 15, 2024	Decision MakingHybrid Machine Learning	—Unverified
Understanding the Structure of QM7b and QM9 Quantum Mechanical Datasets Using Unsupervised Learning	Sep 25, 2023	DescriptiveOutlier Detection	—Unverified
Unified Graph based Multi-Cue Feature Fusion for Robust Visual Tracking	Dec 16, 2018	ObjectObject Tracking	—Unverified

Show:10 25 50

← PrevPage 45 of 71Next →

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