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 541–550 of 703 papers

Title	Date	Tasks	Status
LSCP: Locally Selective Combination in Parallel Outlier Ensembles	Dec 4, 2018	Anomaly DetectionOutlier Detection	CodeCode Available
Concept Learning through Deep Reinforcement Learning with Memory-Augmented Neural Networks	Nov 15, 2018	Deep Reinforcement LearningOne-Shot Learning	—Unverified
Credit Card Fraud Detection in e-Commerce: An Outlier Detection Approach	Nov 6, 2018	ClusteringFraud Detection	—Unverified
Outlier Detection using Generative Models with Theoretical Performance Guarantees	Oct 26, 2018	Outlier Detection	—Unverified
Spectral Embedding Norm: Looking Deep into the Spectrum of the Graph Laplacian	Oct 25, 2018	Anomaly DetectionClustering	CodeCode Available
The JHU Parallel Corpus Filtering Systems for WMT 2018	Oct 1, 2018	Language ModelingLanguage Modelling	—Unverified
The ILSP/ARC submission to the WMT 2018 Parallel Corpus Filtering Shared Task	Oct 1, 2018	ARCClustering	—Unverified
Findings of the WMT 2018 Shared Task on Parallel Corpus Filtering	Oct 1, 2018	Machine TranslationOutlier Detection	—Unverified
STACC, OOV Density and N-gram Saturation: Vicomtech's Participation in the WMT 2018 Shared Task on Parallel Corpus Filtering	Oct 1, 2018	Machine TranslationOutlier Detection	—Unverified
Generative Adversarial Active Learning for Unsupervised Outlier Detection	Sep 28, 2018	Active LearningBinary Classification	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