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 281–290 of 703 papers

Title	Date	Tasks	Status
GAN-RXA: A Practical Scalable Solution to Receiver-Agnostic Transmitter Fingerprinting	Mar 25, 2023	Outlier Detection	—Unverified
Extending Decision Predicate Graphs for Comprehensive Explanation of Isolation Forest	May 6, 2025	Outlier Detection	—Unverified
Cascade Subspace Clustering for Outlier Detection	Jun 23, 2023	ClusteringOutlier Detection	—Unverified
Feature Engineering for Scalable Application-Level Post-Silicon Debugging	Feb 8, 2021	Feature EngineeringOutlier Detection	—Unverified
Feature extraction with regularized siamese networks for outlier detection: application to lesion screening in medical imaging	May 4, 2018	Lesion DetectionOutlier Detection	—Unverified
FedCC: Robust Federated Learning against Model Poisoning Attacks	Dec 5, 2022	Federated Learningmodel	—Unverified
Can Dense Connectivity Benefit Outlier Detection? An Odyssey with NAS	Jun 4, 2024	Neural Architecture SearchOutlier Detection	—Unverified
Exploring Outliers in Crowdsourced Ranking for QoE	Jul 18, 2017	Outlier Detection	—Unverified
Finding Inner Outliers in High Dimensional Space	May 5, 2014	Outlier DetectionVocal Bursts Intensity Prediction	—Unverified
Exploring Information Centrality for Intrusion Detection in Large Networks	Apr 27, 2019	Anomaly DetectionIntrusion 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