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 351–360 of 703 papers

Title	Date	Tasks	Status
Point Cloud Denoising and Outlier Detection with Local Geometric Structure by Dynamic Graph CNN	Oct 11, 2023	DenoisingOutlier Detection	—Unverified
Practical applications of metric space magnitude and weighting vectors	Jun 24, 2020	Active LearningBoundary Detection	—Unverified
Prediction and outlier detection in classification problems	May 10, 2019	ClassificationConformal Prediction	—Unverified
Probabilistic Outlier Detection and Generation	Dec 22, 2020	Outlier Detection	—Unverified
Probabilistic Robust Autoencoders for Outlier Detection	Oct 1, 2021	Anomaly DetectionOutlier Detection	—Unverified
Prompt-driven efficient Open-set Semi-supervised Learning	Sep 28, 2022	Computational EfficiencyOutlier Detection	—Unverified
Proof-of-Contribution-Based Design for Collaborative Machine Learning on Blockchain	Feb 27, 2023	Contribution AssessmentFederated Learning	—Unverified
Provable Self-Representation Based Outlier Detection in a Union of Subspaces	Apr 12, 2017	Outlier Detection	—Unverified
PS-EIP: Robust Photometric Stereo Based on Event Interval Profile	Mar 24, 2025	Deep LearningOutlier Detection	—Unverified
PyOD 2: A Python Library for Outlier Detection with LLM-powered Model Selection	Dec 11, 2024	Anomaly DetectionFraud 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