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 631–640 of 703 papers

Title	Date	Tasks	Status
Cognitive Deep Machine Can Train Itself	Dec 2, 2016	Outlier Detection	—Unverified
Robust k-means: a Theoretical Revisit	Dec 1, 2016	ClusteringOutlier Detection	—Unverified
Deep Learning with Sets and Point Clouds	Nov 14, 2016	ClusteringDeep Learning	—Unverified
Automated Generation of Multilingual Clusters for the Evaluation of Distributed Representations	Nov 4, 2016	Outlier DetectionSentiment Analysis	CodeCode Available
Local Subspace-Based Outlier Detection using Global Neighbourhoods	Nov 1, 2016	Fraud DetectionOutlier Detection	CodeCode Available
Kernel Bandwidth Selection for SVDD: Peak Criterion Approach for Large Data	Oct 31, 2016	Outlier Detection	—Unverified
A Bayesian Ensemble for Unsupervised Anomaly Detection	Oct 24, 2016	Anomaly DetectionClustering	—Unverified
Structured Group Sparsity: A Novel Indoor WLAN Localization, Outlier Detection, and Radio Map Interpolation Scheme	Oct 18, 2016	Indoor LocalizationOutlier Detection	—Unverified
A Joint Indoor WLAN Localization and Outlier Detection Scheme Using LASSO and Elastic-Net Optimization Techniques	Oct 18, 2016	Outlier DetectionPosition	—Unverified
A Robust Framework for Classifying Evolving Document Streams in an Expert-Machine-Crowd Setting	Oct 6, 2016	ClusteringConstrained Clustering	—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