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 101–110 of 703 papers

Title	Date	Tasks	Status	Score
Efficient variational Bayesian neural network ensembles for outlier detection	Mar 20, 2017	Outlier Detection	CodeCode Available	5
Enhancing Traffic Flow Prediction using Outlier-Weighted AutoEncoders: Handling Real-Time Changes	Dec 27, 2023	ManagementOutlier Detection	CodeCode Available	5
Fluctuation-based Outlier Detection	Apr 21, 2022	Outlier Detection	CodeCode Available	5
ast2vec: Utilizing Recursive Neural Encodings of Python Programs	Mar 22, 2021	ClusteringOutlier Detection	CodeCode Available	5
Image Labels Are All You Need for Coarse Seagrass Segmentation	Mar 2, 2023	AllOutlier Detection	CodeCode Available	5
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers	May 30, 2025	Outlier Detection	CodeCode Available	5
A Fast Greedy Algorithm for Outlier Mining	Apr 1, 2006	Outlier Detection	CodeCode Available	5
Distribution and volume based scoring for Isolation Forests	Sep 20, 2023	Outlier Detection	CodeCode Available	5
Autoencoders and Generative Adversarial Networks for Imbalanced Sequence Classification	Jan 8, 2019	Anomaly DetectionClassification	CodeCode Available	5
D.MCA: Outlier Detection with Explicit Micro-Cluster Assignments	Oct 15, 2022	ClusteringOutlier Detection	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