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 481–490 of 703 papers

Title	Date	Tasks	Status
A Comprehensive System for Secondary Structure Analysis of Protein Models	Feb 18, 2024	Outlier Detection	—Unverified
Active Learning of SVDD Hyperparameter Values	Dec 4, 2019	Active LearningOutlier Detection	—Unverified
Active Relation Discovery: Towards General and Label-aware OpenRE	Nov 16, 2021	Active LearningOutlier Detection	—Unverified
Active Relation Discovery: Towards General and Label-aware Open Relation Extraction	Nov 8, 2022	Active LearningOutlier Detection	—Unverified
Adaptive Double-Exploration Tradeoff for Outlier Detection	May 13, 2020	Outlier Detection	—Unverified
Adaptive Outlier Detection for Power MOSFETs Based on Gaussian Process Regression	Jan 25, 2022	GPROutlier Detection	—Unverified
Adaptive PCA-Based Outlier Detection for Multi-Feature Time Series in Space Missions	Apr 22, 2025	Event DetectionOutlier Detection	—Unverified
Incremental Data-driven Optimization of Complex Systems in Nonstationary Environments	Dec 14, 2020	Ensemble LearningOutlier Detection	—Unverified
A Deep Learning Anomaly Detection Method in Textual Data	Nov 25, 2022	Anomaly DetectionDeep Learning	—Unverified
A deep mixture density network for outlier-corrected interpolation of crowd-sourced weather data	Jan 25, 2022	Outlier 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