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 451–460 of 703 papers

Title	Date	Tasks	Status
Generating Artificial Outliers in the Absence of Genuine Ones -- a Survey	Jun 5, 2020	BenchmarkingExperimental Design	—Unverified
Outlier Detection in Indoor Localization and Internet of Things (IoT) using Machine Learning	Jun 3, 2020	BIG-bench Machine LearningEnsemble Learning	—Unverified
Component-wise Adaptive Trimming For Robust Mixture Regression	May 23, 2020	Outlier Detectionparameter estimation	—Unverified
Consistent and Flexible Selectivity Estimation for High-Dimensional Data	May 20, 2020	Data IntegrationDensity Estimation	CodeCode Available
Fair Outlier Detection	May 20, 2020	FairnessOutlier Detection	—Unverified
Variational Hyper-Encoding Networks	May 18, 2020	DecoderDensity Estimation	—Unverified
Adaptive Double-Exploration Tradeoff for Outlier Detection	May 13, 2020	Outlier Detection	—Unverified
Open Set Wireless Transmitter Authorization: Deep Learning Approaches and Dataset Considerations	May 3, 2020	Anomaly DetectionOpen Set Learning	—Unverified
Outlier detection at the parcel-level in wheat and rapeseed crops using multispectral and SAR time series	Apr 17, 2020	Anomaly DetectionOutlier Detection	—Unverified
Benchmarking Unsupervised Outlier Detection with Realistic Synthetic Data	Apr 15, 2020	BenchmarkingOutlier 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