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 411–420 of 703 papers

Title	Date	Tasks	Status	Hype
Autoencoder Watchdog Outlier Detection for Classifiers	Oct 24, 2020	Outlier Detectionregression	—Unverified	0
Rotation Averaging with Attention Graph Neural Networks	Oct 14, 2020	Graph Neural NetworkOutlier Detection	—Unverified	0
Anomaly Detection based on Zero-Shot Outlier Synthesis and Hierarchical Feature Distillation	Oct 10, 2020	Anomaly DetectionOutlier Detection	—Unverified	0
OneFlow: One-class flow for anomaly detection based on a minimal volume region	Oct 6, 2020	Anomaly DetectionOne-class classifier	—Unverified	0
Automating Outlier Detection via Meta-Learning	Sep 22, 2020	Anomaly DetectionAutoML	CodeCode Available	1
Machine learning based forecasting of significant daily returns in foreign exchange markets	Sep 21, 2020	BIG-bench Machine LearningOutlier Detection	—Unverified	0
Robust Outlier Arm Identification	Sep 21, 2020	Outlier Detection	CodeCode Available	0
COPOD: Copula-Based Outlier Detection	Sep 20, 2020	Outlier Detection	CodeCode Available	1
TODS: An Automated Time Series Outlier Detection System	Sep 18, 2020	Outlier DetectionTime Series	CodeCode Available	2
Anomaly Detection With Partitioning Overfitting Autoencoder Ensembles	Sep 6, 2020	Anomaly DetectionOutlier Detection	CodeCode Available	0

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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