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 521–530 of 703 papers

Title	Date	Tasks	Status
Robust Variational Autoencoder	May 23, 2019	Outlier Detection	—Unverified
Automated detection of business-relevant outliers in e-commerce conversion rate	May 15, 2019	Outlier Detection	—Unverified
Prediction and outlier detection in classification problems	May 10, 2019	ClassificationConformal Prediction	—Unverified
Unsupervised routine discovery in egocentric photo-streams	May 10, 2019	Outlier Detection	—Unverified
Outlier Detection from Image Data	May 1, 2019	Autonomous VehiclesGeneral Classification	—Unverified
Exploring Information Centrality for Intrusion Detection in Large Networks	Apr 27, 2019	Anomaly DetectionIntrusion Detection	—Unverified
Graph-Embedded Multi-layer Kernel Extreme Learning Machine for One-class Classification or (Graph-Embedded Multi-layer Kernel Ridge Regression for One-class Classification)	Apr 13, 2019	ClassificationGeneral Classification	—Unverified
Generative Models for Novelty Detection: Applications in abnormal event and situational change detection from data series	Apr 9, 2019	Change DetectionNovelty Detection	—Unverified
Outlier Detection for Improved Data Quality and Diversity in Dialog Systems	Apr 5, 2019	Diversityintent-classification	—Unverified
Learning Representations from Healthcare Time Series Data for Unsupervised Anomaly Detection	Apr 4, 2019	Anomaly DetectionClustering	—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