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 41–50 of 703 papers

Title	Date	Tasks	Status
Blockchain-Empowered Cyber-Secure Federated Learning for Trustworthy Edge Computing	Dec 30, 2024	Edge-computingFairness	—Unverified
Testing and Improving the Robustness of Amortized Bayesian Inference for Cognitive Models	Dec 29, 2024	Bayesian InferenceData Augmentation	CodeCode Available
Brain Ageing Prediction using Isolation Forest Technique and Residual Neural Network (ResNet)	Dec 26, 2024	Age EstimationOutlier Detection	—Unverified
Efficient Curation of Invertebrate Image Datasets Using Feature Embeddings and Automatic Size Comparison	Dec 20, 2024	Outlier Detection	CodeCode Available
PyOD 2: A Python Library for Outlier Detection with LLM-powered Model Selection	Dec 11, 2024	Anomaly DetectionFraud Detection	CodeCode Available
GradStop: Exploring Training Dynamics in Unsupervised Outlier Detection through Gradient	Dec 11, 2024	Anomaly DetectionOutlier Detection	CodeCode Available
Detecting outliers by clustering algorithms	Dec 7, 2024	ClusteringOutlier Detection	—Unverified
Backdooring Outlier Detection Methods: A Novel Attack Approach	Dec 6, 2024	Autonomous DrivingBackdoor Attack	—Unverified
Leveraging Ensemble-Based Semi-Supervised Learning for Illicit Account Detection in Ethereum DeFi Transactions	Dec 3, 2024	Outlier DetectionSelf-Learning	—Unverified
TGTOD: A Global Temporal Graph Transformer for Outlier Detection at Scale	Dec 1, 2024	Link PredictionOutlier Detection	CodeCode Available

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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