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 531–540 of 703 papers

Title	Date	Tasks	Status
On Integrated Clustering and Outlier Detection	Dec 1, 2014	ClusteringOutlier Detection	—Unverified
Onion-Peeling Outlier Detection in 2-D data Sets	Mar 12, 2018	ClusteringFraud Detection	—Unverified
On Language Clustering: A Non-parametric Statistical Approach	Sep 14, 2022	ClusteringOutlier Detection	—Unverified
On Soft Power Diagrams	Jul 15, 2013	ClusteringOutlier Detection	—Unverified
On the Adversarial Robustness of Benjamini Hochberg	Jan 6, 2025	Adversarial RobustnessAnomaly Detection	—Unverified
Open Set Wireless Transmitter Authorization: Deep Learning Approaches and Dataset Considerations	May 3, 2020	Anomaly DetectionOpen Set Learning	—Unverified
Optimization of Retrieval-Augmented Generation Context with Outlier Detection	Jul 1, 2024	Language ModelingLanguage Modelling	—Unverified
Ordinal time series analysis with the R package otsfeatures	Apr 24, 2023	ClusteringOutlier Detection	—Unverified
OuroMamba: A Data-Free Quantization Framework for Vision Mamba Models	Mar 13, 2025	channel selectionContrastive Learning	—Unverified
OutCenTR: A novel semi-supervised framework for predicting exploits of vulnerabilities in high-dimensional datasets	Apr 3, 2023	Dimensionality ReductionOutlier 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