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 421–430 of 703 papers

Title	Date	Tasks	Status
Exploring Outliers in Crowdsourced Ranking for QoE	Jul 18, 2017	Outlier Detection	—Unverified
Extending Decision Predicate Graphs for Comprehensive Explanation of Isolation Forest	May 6, 2025	Outlier Detection	—Unverified
Fairness-aware Outlier Ensemble	Mar 17, 2021	FairnessFraud Detection	—Unverified
Fair Outlier Detection	May 20, 2020	FairnessOutlier Detection	—Unverified
Feature Engineering for Scalable Application-Level Post-Silicon Debugging	Feb 8, 2021	Feature EngineeringOutlier Detection	—Unverified
Feature extraction with regularized siamese networks for outlier detection: application to lesion screening in medical imaging	May 4, 2018	Lesion DetectionOutlier Detection	—Unverified
FedCC: Robust Federated Learning against Model Poisoning Attacks	Dec 5, 2022	Federated Learningmodel	—Unverified
Female mosquito detection by means of AI techniques inside release containers in the context of a Sterile Insect Technique program	Jun 19, 2023	Outlier Detection	—Unverified
Finding Inner Outliers in High Dimensional Space	May 5, 2014	Outlier DetectionVocal Bursts Intensity Prediction	—Unverified
Findings of the WMT 2018 Shared Task on Parallel Corpus Filtering	Oct 1, 2018	Machine TranslationOutlier 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