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 501–510 of 703 papers

Title	Date	Tasks	Status
ALRe: Outlier Detection for Guided Refinement	Aug 1, 2020	Outlier Detection	—Unverified
ALTBI: Constructing Improved Outlier Detection Models via Optimization of Inlier-Memorization Effect	Aug 19, 2024	MemorizationOutlier Detection	—Unverified
A Meta-Learning Algorithm for Interrogative Agendas	Jan 4, 2023	Meta-LearningOutlier Detection	—Unverified
A Model for Spatial Outlier Detection Based on Weighted Neighborhood Relationship	Nov 4, 2019	Decision MakingOutlier Detection	—Unverified
A multi-stream deep neural network with late fuzzy fusion for real-world anomaly detection	Mar 27, 2022	Abnormal Event Detection In VideoAnomaly Detection	—Unverified
Analysis of Learning from Positive and Unlabeled Data	Dec 1, 2014	General ClassificationOutlier Detection	—Unverified
Analyzing categorical time series with the R package ctsfeatures	Apr 24, 2023	ClusteringOutlier Detection	—Unverified
An Approximate Bayesian Long Short-Term Memory Algorithm for Outlier Detection	Dec 23, 2017	Outlier Detection	—Unverified
An Efficient Hashing-based Ensemble Method for Collaborative Outlier Detection	Jan 18, 2022	Outlier Detection	—Unverified
An Efficient Outlier Detection Algorithm for Data Streaming	Jan 2, 2025	Anomaly DetectionFraud 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