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 191–200 of 703 papers

Title	Date	Tasks	Status
Detecting Outliers in Data with Correlated Measures	Aug 26, 2018	Outlier Detection	—Unverified
Decision-change Informed Rejection Improves Robustness in Pattern Recognition-based Myoelectric Control	Sep 21, 2024	Outlier Detection	—Unverified
Deep-Anomaly: Fully Convolutional Neural Network for Fast Anomaly Detection in Crowded Scenes	Sep 3, 2016	Anomaly DetectionOutlier Detection	—Unverified
Cluster Purging: Efficient Outlier Detection based on Rate-Distortion Theory	Feb 22, 2023	ClusteringData Compression	—Unverified
A Non-Parametric Control Chart For High Frequency Multivariate Data	Jul 25, 2016	Outlier DetectionVocal Bursts Intensity Prediction	—Unverified
Deep Learning for Anomaly Detection: A Review	Jul 6, 2020	Anomaly DetectionDeep Learning	—Unverified
Deep Learning for RF Signal Classification in Unknown and Dynamic Spectrum Environments	Sep 25, 2019	blind source separationClassification	—Unverified
Deep Learning with Sets and Point Clouds	Nov 14, 2016	ClusteringDeep Learning	—Unverified
A Robust Regression Approach for Robot Model Learning	Aug 23, 2019	modelOutlier Detection	—Unverified
A Large-scale Study on Unsupervised Outlier Model Selection: Do Internal Strategies Suffice?	Apr 3, 2021	Model SelectionOutlier Detection	—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