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 221–230 of 703 papers

Title	Date	Tasks	Status	Score
Sliced-Wasserstein-based Anomaly Detection and Open Dataset for Localized Critical Peak Rebates	Oct 29, 2024	Anomaly DetectionOutlier Detection	CodeCode Available	5
D.MCA: Outlier Detection with Explicit Micro-Cluster Assignments	Oct 15, 2022	ClusteringOutlier Detection	CodeCode Available	5
Benign Samples Matter! Fine-tuning On Outlier Benign Samples Severely Breaks Safety	May 11, 2025	Outlier DetectionRed Teaming	CodeCode Available	5
Do Ensembling and Meta-Learning Improve Outlier Detection in Randomized Controlled Trials?	Nov 9, 2023	Meta-LearningModel Selection	CodeCode Available	5
Efficient Subspace Search in Data Streams	Nov 13, 2020	Outlier Detection	CodeCode Available	5
Outlier Detection for Text Data : An Extended Version	Jan 5, 2017	AttributeOutlier Detection	CodeCode Available	5
Dimensionality-Aware Outlier Detection: Theoretical and Experimental Analysis	Jan 10, 2024	Outlier Detection	CodeCode Available	5
Automated Generation of Multilingual Clusters for the Evaluation of Distributed Representations	Nov 4, 2016	Outlier DetectionSentiment Analysis	CodeCode Available	5
Automatically detecting anomalous exoplanet transits	Nov 16, 2021	Outlier Detection	CodeCode Available	5
Word Embeddings via Tensor Factorization	Apr 10, 2017	Outlier DetectionWord Embeddings	CodeCode Available	5

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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