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 551–560 of 703 papers

Title	Date	Tasks	Status
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
The JHU Parallel Corpus Filtering Systems for WMT 2018	Oct 1, 2018	Language ModelingLanguage Modelling	—Unverified
Generative Adversarial Active Learning for Unsupervised Outlier Detection	Sep 28, 2018	Active LearningBinary Classification	CodeCode Available
Morpho-MNIST: Quantitative Assessment and Diagnostics for Representation Learning	Sep 27, 2018	DiversityDomain Adaptation	CodeCode Available
Characterizing Malicious Edges targeting on Graph Neural Networks	Sep 27, 2018	Graph GenerationLink Prediction	—Unverified
Using Eigencentrality to Estimate Joint, Conditional and Marginal Probabilities from Mixed-Variable Data: Method and Applications	Sep 19, 2018	AttributeBIG-bench Machine Learning	—Unverified
Online Cyber-Attack Detection in Smart Grid: A Reinforcement Learning Approach	Sep 14, 2018	Anomaly DetectionCyber Attack Detection	CodeCode Available
Sparse Kernel PCA for Outlier Detection	Sep 7, 2018	Outlier Detection	CodeCode Available
Detecting Outliers in Data with Correlated Measures	Aug 26, 2018	Outlier Detection	—Unverified

Show:10 25 50

← PrevPage 56 of 71Next →

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