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 531–540 of 703 papers

Title	Date	Tasks	Status	Hype
Learning Regularity in Skeleton Trajectories for Anomaly Detection in Videos	Mar 8, 2019	Anomaly DetectionDecoder	CodeCode Available	0
Explaining Anomalies Detected by Autoencoders Using SHAP	Mar 6, 2019	Anomaly DetectionOutlier Detection	CodeCode Available	1
Anomaly Detection for an E-commerce Pricing System	Feb 25, 2019	Anomaly DetectionModel Selection	—Unverified	0
Truncated Gaussian-Mixture Variational AutoEncoder	Feb 11, 2019	ClusteringFunctional Connectivity	—Unverified	0
ELKI: A large open-source library for data analysis - ELKI Release 0.7.5 "Heidelberg"	Feb 10, 2019	BenchmarkingClustering	—Unverified	0
Autoencoders and Generative Adversarial Networks for Imbalanced Sequence Classification	Jan 8, 2019	Anomaly DetectionClassification	CodeCode Available	0
PyOD: A Python Toolbox for Scalable Outlier Detection	Jan 6, 2019	Anomaly DetectionOutlier Detection	CodeCode Available	1
Unified Graph based Multi-Cue Feature Fusion for Robust Visual Tracking	Dec 16, 2018	ObjectObject Tracking	—Unverified	0
Object-centric Auto-encoders and Dummy Anomalies for Abnormal Event Detection in Video	Dec 11, 2018	Abnormal Event Detection In VideoAnomaly Detection	CodeCode Available	0
Robust Ordinal Embedding from Contaminated Relative Comparisons	Dec 5, 2018	Outlier Detection	CodeCode Available	0

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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