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 471–480 of 703 papers

Title	Date	Tasks	Status
When Complexity Is Good: Do We Need Recurrent Deep Learning For Time Series Outlier Detection?	Sep 29, 2021	Home Activity MonitoringOutlier Detection	—Unverified
Zero-shot Outlier Detection via Prior-data Fitted Networks: Model Selection Bygone!	Sep 9, 2024	Model SelectionOutlier Detection	—Unverified
Zipporah: a Fast and Scalable Data Cleaning System for Noisy Web-Crawled Parallel Corpora	Sep 1, 2017	Language ModelingLanguage Modelling	—Unverified
Consensus Clustering: An Embedding Perspective, Extension and Beyond	May 31, 2019	ClusteringConstrained Clustering	—Unverified
3D Labeling Tool	Jul 23, 2022	Objectobject-detection	—Unverified
3D Scanning: A Comprehensive Survey	Jan 24, 2018	Outlier DetectionSurvey	—Unverified
A Bayesian Ensemble for Unsupervised Anomaly Detection	Oct 24, 2016	Anomaly DetectionClustering	—Unverified
A boosted outlier detection method based on the spectrum of the Laplacian matrix of a graph	Aug 7, 2020	ClusteringOutlier Detection	—Unverified
About Test-time training for outlier detection	Apr 4, 2024	Outlier Detection	—Unverified
Achieving differential privacy for k-nearest neighbors based outlier detection by data partitioning	Apr 16, 2021	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