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 401–410 of 703 papers

Title	Date	Tasks	Status
Sampling Method for Fast Training of Support Vector Data Description	Jun 16, 2016	Outlier Detection	—Unverified
Scalable Support Vector Clustering Using Budget	Sep 19, 2017	ClusteringOutlier Detection	—Unverified
Self-Optimizing Feature Transformation	Sep 16, 2022	Feature EngineeringOutlier Detection	—Unverified
Semantic Driven Energy based Out-of-Distribution Detection	Aug 23, 2022	object-detectionObject Detection	—Unverified
Semi-supervised Outlier Detection using Generative And Adversary Framework	Jan 1, 2018	General ClassificationMulti-class Classification	—Unverified
Separability and Scatteredness (S&S) Ratio-Based Efficient SVM Regularization Parameter, Kernel, and Kernel Parameter Selection	May 17, 2023	Outlier Detection	—Unverified
Sequential Ensemble Learning for Outlier Detection: A Bias-Variance Perspective	Sep 18, 2016	Binary ClassificationClustering	—Unverified
Sequential Outlier Detection based on Incremental Decision Trees	Mar 9, 2018	Outlier Detection	—Unverified
Shaping Level Sets with Submodular Functions	Dec 1, 2011	Change Point DetectionClustering	—Unverified
Similarity- based approach for outlier detection	Nov 25, 2014	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