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 491–500 of 703 papers

Title	Date	Tasks	Status
Learning Representations for Outlier Detection on a Budget	Jul 29, 2015	Fraud DetectionOutlier Detection	—Unverified
Learning Representations from Healthcare Time Series Data for Unsupervised Anomaly Detection	Apr 4, 2019	Anomaly DetectionClustering	—Unverified
LEARNING SEMANTIC WORD RESPRESENTATIONS VIA TENSOR FACTORIZATION	Jan 1, 2018	Outlier Detection	—Unverified
Learning to Detect Interesting Anomalies	Oct 28, 2022	Active LearningAnomaly Detection	—Unverified
Learning to Rank Anomalies: Scalar Performance Criteria and Maximization of Two-Sample Rank Statistics	Sep 20, 2021	Binary ClassificationFraud Detection	—Unverified
Learning Universe Model for Partial Matching Networks over Multiple Graphs	Oct 19, 2022	Graph MatchingMetric Learning	—Unverified
Let me join you! Real-time F-formation recognition by a socially aware robot	Aug 23, 2020	Outlier Detection	—Unverified
Leveraging Ensemble-Based Semi-Supervised Learning for Illicit Account Detection in Ethereum DeFi Transactions	Dec 3, 2024	Outlier DetectionSelf-Learning	—Unverified
Lifted Coefficient of Determination: Fast model-free prediction intervals and likelihood-free model comparison	Oct 11, 2024	modelOutlier Detection	—Unverified
Linear-time Outlier Detection via Sensitivity	May 2, 2016	ClusteringOutlier 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