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 121–130 of 703 papers

Title	Date	Tasks	Status
Outlier detection using flexible categorisation and interrogative agendas	Dec 19, 2023	Meta-LearningOutlier Detection	—Unverified
A Hybrid Intelligent Framework for Maximising SAG Mill Throughput: An Integration of Expert Knowledge, Machine Learning and Evolutionary Algorithms for Parameter Optimisation	Dec 18, 2023	Evolutionary Algorithmsfeature selection	—Unverified
Ocean Data Quality Assessment through Outlier Detection-enhanced Active Learning	Dec 17, 2023	Active LearningOutlier Detection	—Unverified
RANRAC: Robust Neural Scene Representations via Random Ray Consensus	Dec 15, 2023	Novel View SynthesisOutlier Detection	—Unverified
Meta-survey on outlier and anomaly detection	Dec 12, 2023	Anomaly DetectionBenchmarking	CodeCode Available
HLoOP -- Hyperbolic 2-space Local Outlier Probabilities	Dec 6, 2023	Outlier Detection	—Unverified
Enhancing Sentiment Analysis Results through Outlier Detection Optimization	Nov 25, 2023	Language ModelingLanguage Modelling	—Unverified
Local Concept Embeddings for Analysis of Concept Distributions in Vision DNN Feature Spaces	Nov 24, 2023	Explainable artificial intelligenceExplainable Artificial Intelligence (XAI)	CodeCode Available
Enhancing Intrusion Detection In Internet Of Vehicles Through Federated Learning	Nov 23, 2023	Federated LearningIntrusion Detection	—Unverified
ODDR: Outlier Detection & Dimension Reduction Based Defense Against Adversarial Patches	Nov 20, 2023	Depth EstimationDimensionality Reduction	—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