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 351–360 of 703 papers

Title	Date	Tasks	Status
Anomaly Rule Detection in Sequence Data	Nov 29, 2021	Anomaly DetectionOutlier Detection	—Unverified
Outlier Detection for Trajectories via Flow-embeddings	Nov 25, 2021	Outlier Detection	CodeCode Available
Isolation forests: looking beyond tree depth	Nov 23, 2021	Outlier Detection	—Unverified
Unsupervised Time Series Outlier Detection with Diversity-Driven Convolutional Ensembles -- Extended Version	Nov 22, 2021	DiversityOutlier Detection	—Unverified
Active Relation Discovery: Towards General and Label-aware OpenRE	Nov 16, 2021	Active LearningOutlier Detection	—Unverified
Outlier Detection as Instance Selection Method for Feature Selection in Time Series Classification	Nov 16, 2021	feature selectionOutlier Detection	—Unverified
Automatically detecting anomalous exoplanet transits	Nov 16, 2021	Outlier Detection	CodeCode Available
Mathematical Models for Local Sensing Hashes	Nov 16, 2021	ClusteringOutlier Detection	—Unverified
Contextual Unsupervised Outlier Detection in Sequences	Nov 6, 2021	Outlier Detection	—Unverified
Real-time Wireless Transmitter Authorization: Adapting to Dynamic Authorized Sets with Information Retrieval	Nov 4, 2021	Deep LearningDimensionality Reduction	—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