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 401–410 of 703 papers

Title	Date	Tasks	Status
An Isolation Forest Learning Based Outlier Detection Approach for Effectively Classifying Cyber Anomalies	Dec 9, 2020	Intrusion DetectionOutlier Detection	—Unverified
JANUS: Benchmarking Commercial and Open-Source Cloud and Edge Platforms for Object and Anomaly Detection Workloads	Dec 9, 2020	Anomaly DetectionBenchmarking	—Unverified
FairOD: Fairness-aware Outlier Detection	Dec 5, 2020	FairnessOutlier Detection	CodeCode Available
Nowcasting Networks	Nov 27, 2020	Outlier DetectionTime Series	CodeCode Available
Dense open-set recognition with synthetic outliers generated by Real NVP	Nov 22, 2020	Autonomous Drivingimage-classification	CodeCode Available
A Knowledge Distillation Ensemble Framework for Predicting Short and Long-term Hospitalisation Outcomes from Electronic Health Records Data	Nov 18, 2020	Decision MakingICU Admission	CodeCode Available
Probing Predictions on OOD Images via Nearest Categories	Nov 17, 2020	Adversarial RobustnessFew-Shot Learning	CodeCode Available
Efficient Subspace Search in Data Streams	Nov 13, 2020	Outlier Detection	CodeCode Available
Toward Scalable and Unified Example-based Explanation and Outlier Detection	Nov 11, 2020	Decision MakingOutlier Detection	—Unverified
Further Analysis of Outlier Detection with Deep Generative Models	Oct 25, 2020	Outlier 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