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 381–390 of 703 papers

Title	Date	Tasks	Status	Hype
Impacts of the Numbers of Colors and Shapes on Outlier Detection: from Automated to User Evaluation	Mar 10, 2021	AttributeOutlier Detection	—Unverified	0
Symbiotic Hybrid Neural Network Watchdog For Outlier Detection	Feb 28, 2021	Outlier Detection	—Unverified	0
Tax Evasion Risk Management Using a Hybrid Unsupervised Outlier Detection Method	Feb 25, 2021	ClusteringFraud Detection	—Unverified	0
Elastic Similarity and Distance Measures for Multivariate Time Series	Feb 20, 2021	ClassificationDynamic Time Warping	CodeCode Available	0
DEUP: Direct Epistemic Uncertainty Prediction	Feb 16, 2021	Active Learningimage-classification	CodeCode Available	1
Clustered Hierarchical Anomaly and Outlier Detection Algorithms	Feb 9, 2021	Anomaly DetectionClustering	CodeCode Available	1
Feature Engineering for Scalable Application-Level Post-Silicon Debugging	Feb 8, 2021	Feature EngineeringOutlier Detection	—Unverified	0
RECol: Reconstruction Error Columns for Outlier Detection	Feb 4, 2021	Outlier Detection	—Unverified	0
Measuring Dependence with Matrix-based Entropy Functional	Jan 25, 2021	BIG-bench Machine LearningOutlier Detection	CodeCode Available	0
Dense outlier detection and open-set recognition based on training with noisy negative images	Jan 22, 2021	Open Set LearningOutlier Detection	—Unverified	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