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 561–570 of 703 papers

Title	Date	Tasks	Status
AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning	Jun 19, 2020	Fraud Detectionimage-classification	—Unverified
AWT -- Clustering Meteorological Time Series Using an Aggregated Wavelet Tree	Dec 13, 2022	ClusteringOutlier Detection	—Unverified
Backdoor Defense in Federated Learning Using Differential Testing and Outlier Detection	Feb 21, 2022	backdoor defenseFederated Learning	—Unverified
Backdooring Outlier Detection Methods: A Novel Attack Approach	Dec 6, 2024	Autonomous DrivingBackdoor Attack	—Unverified
BAHP: Benchmark of Assessing Word Embeddings in Historical Portuguese	Nov 1, 2021	Outlier DetectionWord Embeddings	—Unverified
Benchmarking Unsupervised Outlier Detection with Realistic Synthetic Data	Apr 15, 2020	BenchmarkingOutlier Detection	—Unverified
Blind Image Deblurring With Outlier Handling	Oct 1, 2017	Blind Image DeblurringDeblurring	—Unverified
Blockchain-Empowered Cyber-Secure Federated Learning for Trustworthy Edge Computing	Dec 30, 2024	Edge-computingFairness	—Unverified
BoBa: Boosting Backdoor Detection through Data Distribution Inference in Federated Learning	Jul 12, 2024	Anomaly DetectionBackdoor Attack	—Unverified
Boundary Peeling: Outlier Detection Method Using One-Class Peeling	Sep 11, 2023	Outlier 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