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 181–190 of 703 papers

Title	Date	Tasks	Status
Detecting Point Outliers Using Prune-based Outlier Factor (PLOF)	Nov 5, 2019	Anomaly DetectionOutlier Detection	—Unverified
Coverage-based Outlier Explanation	Nov 6, 2019	Outlier Detection	—Unverified
Credit Card Fraud Detection in e-Commerce: An Outlier Detection Approach	Nov 6, 2018	ClusteringFraud Detection	—Unverified
Cross Domain Image Matching in Presence of Outliers	Sep 8, 2019	Domain AdaptationOutlier Detection	—Unverified
Detect Professional Malicious User with Metric Learning in Recommender Systems	May 19, 2022	Metric LearningOutlier Detection	—Unverified
A Review of Change of Variable Formulas for Generative Modeling	Aug 4, 2023	Bayesian InferenceDecoder	—Unverified
A Review of Graph-Powered Data Quality Applications for IoT Monitoring Sensor Networks	Oct 28, 2024	ImputationOutlier Detection	—Unverified
Data Enrichment Opportunities for Distribution Grid Cable Networks using Variational Autoencoders	Jan 19, 2025	Feature ImportanceImputation	—Unverified
Data refinement for fully unsupervised visual inspection using pre-trained networks	Feb 25, 2022	Anomaly DetectionOne-Class Classification	—Unverified
AI-enabled Blockchain: An Outlier-aware Consensus Protocol for Blockchain-based IoT Networks	Jun 17, 2019	BIG-bench Machine LearningOutlier 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