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 681–690 of 703 papers

Title	Date	Tasks	Status
Unsupervised Boosting-based Autoencoder Ensembles for Outlier Detection	Oct 22, 2019	Dimensionality ReductionDiversity	CodeCode Available
G-PECNet: Towards a Generalizable Pedestrian Trajectory Prediction System	Oct 15, 2022	Autonomous VehiclesData Augmentation	CodeCode Available
A geometric perspective on functional outlier detection	Sep 14, 2021	Outlier Detection	CodeCode Available
Event Outlier Detection in Continuous Time	Dec 19, 2019	Outlier DetectionPoint Processes	CodeCode Available
Statistical Test for Feature Selection Pipelines by Selective Inference	Jun 27, 2024	feature selectionImputation	CodeCode Available
Robust Ordinal Embedding from Contaminated Relative Comparisons	Dec 5, 2018	Outlier Detection	CodeCode Available
Robust Outlier Arm Identification	Sep 21, 2020	Outlier Detection	CodeCode Available
Outlier-Insensitive Kalman Filtering: Theory and Applications	Sep 18, 2023	Outlier DetectionState Estimation	CodeCode Available
Local Subspace-Based Outlier Detection using Global Neighbourhoods	Nov 1, 2016	Fraud DetectionOutlier Detection	CodeCode Available
MaxGap Bandit: Adaptive Algorithms for Approximate Ranking	Jun 3, 2019	Outlier Detection	CodeCode Available

Show:10 25 50

← PrevPage 69 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