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 331–340 of 703 papers

Title	Date	Tasks	Status
A Bayesian Ensemble for Unsupervised Anomaly Detection	Oct 24, 2016	Anomaly DetectionClustering	—Unverified
Identifying Informative Latent Variables Learned by GIN via Mutual Information	Jan 1, 2021	Adversarial AttackDisentanglement	—Unverified
Identifying Outlier Arms in Multi-Armed Bandit	Dec 1, 2017	Outlier Detection	—Unverified
Identifying Semantically Deviating Outlier Documents	Sep 1, 2017	Outlier Detection	—Unverified
Isolation forests: looking beyond tree depth	Nov 23, 2021	Outlier Detection	—Unverified
Concept Learning through Deep Reinforcement Learning with Memory-Augmented Neural Networks	Nov 15, 2018	Deep Reinforcement LearningOne-Shot Learning	—Unverified
Image Modeling with Deep Convolutional Gaussian Mixture Models	Apr 19, 2021	ClusteringOutlier Detection	—Unverified
Conditional Selective Inference for Robust Regression and Outlier Detection using Piecewise-Linear Homotopy Continuation	Apr 22, 2021	Outlier Detectionregression	—Unverified
Impacts of the Numbers of Colors and Shapes on Outlier Detection: from Automated to User Evaluation	Mar 10, 2021	AttributeOutlier Detection	—Unverified
Exact Subspace Segmentation and Outlier Detection by Low-Rank Representation	Sep 8, 2011	Outlier Detection	—Unverified

Show:10 25 50

← PrevPage 34 of 71Next →

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