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 61–70 of 703 papers

Title	Date	Tasks	Status	Hype
Learning Energy-Based Models in High-Dimensional Spaces with Multi-scale Denoising Score Matching	Oct 17, 2019	DenoisingImage Inpainting	CodeCode Available	1
SUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier Detection	Mar 11, 2020	Dimensionality ReductionFraud Detection	CodeCode Available	1
Explainable Deep One-Class Classification	Jul 3, 2020	Anomaly DetectionClassification	CodeCode Available	1
Unsupervised Graph Outlier Detection: Problem Revisit, New Insight, and Superior Method	Oct 24, 2022	AttributeGraph Outlier Detection	CodeCode Available	1
Autoencoding Under Normalization Constraints	May 12, 2021	Outlier Detection	CodeCode Available	1
Automating Outlier Detection via Meta-Learning	Sep 22, 2020	Anomaly DetectionAutoML	CodeCode Available	1
Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Control	Oct 3, 2021	BIG-bench Machine LearningInstance Segmentation	CodeCode Available	1
Computationally Assisted Quality Control for Public Health Data Streams	Jun 29, 2023	Decision MakingOutlier Detection	CodeCode Available	1
COPOD: Copula-Based Outlier Detection	Sep 20, 2020	Outlier Detection	CodeCode Available	1
Toward Unsupervised Outlier Model Selection	Nov 3, 2022	Meta-Learningmodel	CodeCode Available	1

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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