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	Hype
DRGRADUATE: uncertainty-aware deep learning-based diabetic retinopathy grading in eye fundus images	Oct 25, 2019	Diabetic Retinopathy GradingMultiple Instance Learning	—Unverified	0
Trojan Attacks on Wireless Signal Classification with Adversarial Machine Learning	Oct 23, 2019	BIG-bench Machine LearningClassification	—Unverified	0
Unsupervised Boosting-based Autoencoder Ensembles for Outlier Detection	Oct 22, 2019	Dimensionality ReductionDiversity	CodeCode Available	0
Learning Energy-Based Models in High-Dimensional Spaces with Multi-scale Denoising Score Matching	Oct 17, 2019	DenoisingImage Inpainting	CodeCode Available	1
MSD-Kmeans: A Novel Algorithm for Efficient Detection of Global and Local Outliers	Oct 15, 2019	ClusteringOutlier Detection	—Unverified	0
Estimating Density Models with Truncation Boundaries using Score Matching	Oct 9, 2019	Density EstimationOutlier Detection	CodeCode Available	0
PyODDS: An End-to-End Outlier Detection System	Oct 7, 2019	BIG-bench Machine LearningOutlier Detection	CodeCode Available	0
Versatile Anomaly Detection with Outlier Preserving Distribution Mapping Autoencoders	Sep 25, 2019	Anomaly DetectionOutlier Detection	—Unverified	0
Annealed Denoising score matching: learning Energy based model in high-dimensional spaces	Sep 25, 2019	DenoisingImage Inpainting	—Unverified	0
Deep Learning for RF Signal Classification in Unknown and Dynamic Spectrum Environments	Sep 25, 2019	blind source separationClassification	—Unverified	0

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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