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 151–160 of 703 papers

Title	Date	Tasks	Status
Incremental Data-driven Optimization of Complex Systems in Nonstationary Environments	Dec 14, 2020	Ensemble LearningOutlier Detection	—Unverified
Applications of a Graph Theoretic Based Clustering Framework in Computer Vision and Pattern Recognition	Jan 7, 2018	Clusteringgeo-localization	—Unverified
A Local Density-Based Approach for Local Outlier Detection	Jun 28, 2016	Density EstimationObject	—Unverified
Contextual Unsupervised Outlier Detection in Sequences	Nov 6, 2021	Outlier Detection	—Unverified
Adaptive PCA-Based Outlier Detection for Multi-Feature Time Series in Space Missions	Apr 22, 2025	Event DetectionOutlier Detection	—Unverified
Combining Structured and Unstructured Randomness in Large Scale PCA	Oct 23, 2013	BIG-bench Machine LearningOutlier Detection	—Unverified
An Outlier Detection-based Tree Selection Approach to Extreme Pruning of Random Forests	Mar 17, 2015	Ensemble PruningOutlier Detection	—Unverified
About Test-time training for outlier detection	Apr 4, 2024	Outlier Detection	—Unverified
Continual Learning with Fully Probabilistic Models	Apr 19, 2021	Boundary Detectionclass-incremental learning	—Unverified
Cross Domain Image Matching in Presence of Outliers	Sep 8, 2019	Domain AdaptationOutlier 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