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 491–500 of 703 papers

Title	Date	Tasks	Status
Deep Variational Semi-Supervised Novelty Detection	Nov 12, 2019	Anomaly DetectionAstronomy	—Unverified
Coverage-based Outlier Explanation	Nov 6, 2019	Outlier Detection	—Unverified
Detecting Point Outliers Using Prune-based Outlier Factor (PLOF)	Nov 5, 2019	Anomaly DetectionOutlier Detection	—Unverified
A Model for Spatial Outlier Detection Based on Weighted Neighborhood Relationship	Nov 4, 2019	Decision MakingOutlier Detection	—Unverified
MIX: A Joint Learning Framework for Detecting Both Clustered and Scattered Outliers in Mixed-Type Data	Nov 1, 2019	Anomaly DetectionOutlier Detection	CodeCode Available
Are Out-of-Distribution Detection Methods Effective on Large-Scale Datasets?	Oct 30, 2019	General Classificationimage-classification	—Unverified
Distance approximation using Isolation Forests	Oct 27, 2019	Missing ValuesOutlier Detection	—Unverified
DRGRADUATE: uncertainty-aware deep learning-based diabetic retinopathy grading in eye fundus images	Oct 25, 2019	Diabetic Retinopathy GradingMultiple Instance Learning	—Unverified
Trojan Attacks on Wireless Signal Classification with Adversarial Machine Learning	Oct 23, 2019	BIG-bench Machine LearningClassification	—Unverified
Unsupervised Boosting-based Autoencoder Ensembles for Outlier Detection	Oct 22, 2019	Dimensionality ReductionDiversity	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