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 501–510 of 703 papers

Title	Date	Tasks	Status
GENDIS: GENetic DIscovery of Shapelets	Sep 13, 2019	Outlier DetectionTime Series	CodeCode Available
A Study of Deep Learning for Network Traffic Data Forecasting	Sep 10, 2019	ClusteringDeep Learning	—Unverified
Outlier Detection in High Dimensional Data	Sep 9, 2019	Density EstimationOutlier Detection	—Unverified
Cross Domain Image Matching in Presence of Outliers	Sep 8, 2019	Domain AdaptationOutlier Detection	—Unverified
A Robust Regression Approach for Robot Model Learning	Aug 23, 2019	modelOutlier Detection	—Unverified
What goes around comes around: Cycle-Consistency-based Short-Term Motion Prediction for Anomaly Detection using Generative Adversarial Networks	Aug 8, 2019	Anomaly DetectionGenerative Adversarial Network	—Unverified
Simultaneous Semantic Segmentation and Outlier Detection in Presence of Domain Shift	Aug 3, 2019	Anomaly DetectionDiversity	CodeCode Available
Are Outlier Detection Methods Resilient to Sampling?	Jul 31, 2019	Outlier Detection	—Unverified
MIMIC-Extract: A Data Extraction, Preprocessing, and Representation Pipeline for MIMIC-III	Jul 19, 2019	BIG-bench Machine LearningLength-of-Stay prediction	CodeCode Available
Robust Variational Autoencoders for Outlier Detection and Repair of Mixed-Type Data	Jul 15, 2019	ImputationOutlier Detection	CodeCode Available

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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