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 651–660 of 703 papers

Title	Date	Tasks	Status
A Local Density-Based Approach for Local Outlier Detection	Jun 28, 2016	Density EstimationObject	—Unverified
Holistic Features For Real-Time Crowd Behaviour Anomaly Detection	Jun 16, 2016	Anomaly DetectionBinary Classification	—Unverified
Sampling Method for Fast Training of Support Vector Data Description	Jun 16, 2016	Outlier Detection	—Unverified
A Scalable Approach for Outlier Detection in Edge Streams Using Sketch-based Approximations	May 7, 2016	Anomaly DetectionAnomaly Detection in Edge Streams	—Unverified
Single Channel Speech Enhancement Using Outlier Detection	May 4, 2016	Noise EstimationOutlier Detection	—Unverified
Linear-time Outlier Detection via Sensitivity	May 2, 2016	ClusteringOutlier Detection	—Unverified
Distance for Functional Data Clustering Based on Smoothing Parameter Commutation	Apr 10, 2016	ClusteringMissing Values	—Unverified
Depth-Based Object Tracking Using a Robust Gaussian Filter	Feb 19, 2016	Computational EfficiencyObject	CodeCode Available
Peak Criterion for Choosing Gaussian Kernel Bandwidth in Support Vector Data Description	Feb 17, 2016	General ClassificationOutlier Detection	—Unverified
GraphPrints: Towards a Graph Analytic Method for Network Anomaly Detection	Feb 2, 2016	Anomaly DetectionOutlier Detection	—Unverified

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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