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
Anomaly Detection based on Zero-Shot Outlier Synthesis and Hierarchical Feature Distillation	Oct 10, 2020	Anomaly DetectionOutlier Detection	—Unverified
Anomaly Detection by Robust Statistics	Jul 31, 2017	Anomaly DetectionClustering	—Unverified
Anomaly Detection for an E-commerce Pricing System	Feb 25, 2019	Anomaly DetectionModel Selection	—Unverified
Anomaly Detection for Unmanned Aerial Vehicle Sensor Data Using a Stacked Recurrent Autoencoder Method with Dynamic Thresholding	Mar 9, 2022	Anomaly DetectionFault Detection	—Unverified
Anomaly Detection using Capsule Networks for High-dimensional Datasets	Dec 27, 2021	Anomaly DetectionBinary Classification	—Unverified
Positive Difference Distribution for Image Outlier Detection using Normalizing Flows and Contrastive Data	Aug 30, 2022	Anomaly DetectionOutlier Detection	—Unverified
Anomaly Detection with HMM Gauge Likelihood Analysis	Jun 14, 2019	Anomaly DetectionClustering	—Unverified
Anomaly-Injected Deep Support Vector Data Description for Text Outlier Detection	Oct 27, 2021	Anomaly DetectionOutlier Detection	—Unverified
Anomaly Rule Detection in Sequence Data	Nov 29, 2021	Anomaly DetectionOutlier Detection	—Unverified
A Non-Parametric Control Chart For High Frequency Multivariate Data	Jul 25, 2016	Outlier DetectionVocal Bursts Intensity Prediction	—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