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 271–280 of 703 papers

Title	Date	Tasks	Status
DOC-NAD: A Hybrid Deep One-class Classifier for Network Anomaly Detection	Dec 15, 2022	Anomaly DetectionIntrusion Detection	—Unverified
Learning Markerless Robot-Depth Camera Calibration and End-Effector Pose Estimation	Dec 15, 2022	Camera CalibrationKeypoint Detection	—Unverified
AWT -- Clustering Meteorological Time Series Using an Aggregated Wavelet Tree	Dec 13, 2022	ClusteringOutlier Detection	—Unverified
FedCC: Robust Federated Learning against Model Poisoning Attacks	Dec 5, 2022	Federated Learningmodel	—Unverified
A Deep Learning Anomaly Detection Method in Textual Data	Nov 25, 2022	Anomaly DetectionDeep Learning	—Unverified
CoMadOut -- A Robust Outlier Detection Algorithm based on CoMAD	Nov 23, 2022	Anomaly DetectionOutlier Detection	CodeCode Available
Active Relation Discovery: Towards General and Label-aware Open Relation Extraction	Nov 8, 2022	Active LearningOutlier Detection	—Unverified
ODBAE: a high-performance model identifying complex phenotypes in high-dimensional biological datasets	Nov 6, 2022	Outlier Detectionregression	—Unverified
Meta-Learning for Unsupervised Outlier Detection with Optimal Transport	Nov 1, 2022	Meta-LearningOutlier Detection	—Unverified
Learning to Detect Interesting Anomalies	Oct 28, 2022	Active LearningAnomaly 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