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 191–200 of 703 papers

Title	Date	Tasks	Status
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
PACOL: Poisoning Attacks Against Continual Learners	Nov 18, 2023	Continual LearningData Poisoning	—Unverified
SSB: Simple but Strong Baseline for Boosting Performance of Open-Set Semi-Supervised Learning	Nov 17, 2023	Multi-Task LearningOutlier Detection	CodeCode Available
Do Ensembling and Meta-Learning Improve Outlier Detection in Randomized Controlled Trials?	Nov 9, 2023	Meta-LearningModel Selection	CodeCode Available
IoT-Based Environmental Control System for Fish Farms with Sensor Integration and Machine Learning Decision Support	Nov 7, 2023	Feature EngineeringImputation	—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