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 661–670 of 703 papers

Title	Date	Tasks	Status
Outlier Detection In Large-scale Traffic Data By Naïve Bayes Method and Gaussian Mixture Model Method	Dec 28, 2015	Dimensionality ReductionManagement	—Unverified
Robust Classification by Pre-conditioned LASSO and Transductive Diffusion Component Analysis	Nov 19, 2015	BIG-bench Machine LearningClassification	—Unverified
A Framework for Clustering Uncertain Data	Aug 1, 2015	ClusteringOutlier Detection	—Unverified
Learning Representations for Outlier Detection on a Budget	Jul 29, 2015	Fraud DetectionOutlier Detection	—Unverified
Differentially Private Analysis of Outliers	Jul 24, 2015	Outlier DetectionSensitivity	—Unverified
Diffusion Nets	Jun 25, 2015	DecoderOutlier Detection	—Unverified
MCODE: Multivariate Conditional Outlier Detection	May 15, 2015	General ClassificationOutlier Detection	—Unverified
An Outlier Detection-based Tree Selection Approach to Extreme Pruning of Random Forests	Mar 17, 2015	Ensemble PruningOutlier Detection	—Unverified
Random Subspace Learning Approach to High-Dimensional Outliers Detection	Feb 16, 2015	Outlier DetectionVocal Bursts Intensity Prediction	—Unverified
Robust Subjective Visual Property Prediction from Crowdsourced Pairwise Labels	Jan 25, 2015	AttributeLearning-To-Rank	—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