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 81–90 of 703 papers

Title	Date	Tasks	Status	Score
A Framework for Clustering Uncertain Data	Aug 1, 2015	ClusteringOutlier Detection	CodeCode Available	5
Robust outlier detection by de-biasing VAE likelihoods	Aug 19, 2021	DecoderOutlier Detection	CodeCode Available	5
Effective End-to-end Unsupervised Outlier Detection via Inlier Priority of Discriminative Network	Dec 1, 2019	Outlier DetectionRepresentation Learning	CodeCode Available	5
Edgewise outliers of network indexed signals	Jul 20, 2023	Outlier Detection	CodeCode Available	5
Efficient Curation of Invertebrate Image Datasets Using Feature Embeddings and Automatic Size Comparison	Dec 20, 2024	Outlier Detection	CodeCode Available	5
Efficient Subspace Search in Data Streams	Nov 13, 2020	Outlier Detection	CodeCode Available	5
Enhancing Traffic Flow Prediction using Outlier-Weighted AutoEncoders: Handling Real-Time Changes	Dec 27, 2023	ManagementOutlier Detection	CodeCode Available	5
D.MCA: Outlier Detection with Explicit Micro-Cluster Assignments	Oct 15, 2022	ClusteringOutlier Detection	CodeCode Available	5
A Fast Greedy Algorithm for Outlier Mining	Apr 1, 2006	Outlier Detection	CodeCode Available	5
Do Ensembling and Meta-Learning Improve Outlier Detection in Randomized Controlled Trials?	Nov 9, 2023	Meta-LearningModel Selection	CodeCode Available	5

Show:10 25 50

← PrevPage 9 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