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
Explainable Deep One-Class Classification	Jul 3, 2020	Anomaly DetectionClassification	CodeCode Available	1
Unknown Intent Detection Using Gaussian Mixture Model with an Application to Zero-shot Intent Classification	Jul 1, 2020	ClassificationGeneral Classification	CodeCode Available	1
Probabilistic Autoencoder	Jun 9, 2020	Bayesian InferenceDenoising	CodeCode Available	1
Picket: Guarding Against Corrupted Data in Tabular Data during Learning and Inference	Jun 8, 2020	BIG-bench Machine LearningOutlier Detection	CodeCode Available	1
AdaLAM: Revisiting Handcrafted Outlier Detection	Jun 7, 2020	Key Point MatchingOutlier Detection	CodeCode Available	1
SUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier Detection	Mar 11, 2020	Dimensionality ReductionFraud Detection	CodeCode Available	1
SUOD: Toward Scalable Unsupervised Outlier Detection	Feb 8, 2020	Knowledge DistillationOutlier Detection	CodeCode Available	1
Explainable outlier detection through decision tree conditioning	Jan 2, 2020	Outlier Detection	CodeCode Available	1
Learning Energy-Based Models in High-Dimensional Spaces with Multi-scale Denoising Score Matching	Oct 17, 2019	DenoisingImage Inpainting	CodeCode Available	1
Explaining Anomalies Detected by Autoencoders Using SHAP	Mar 6, 2019	Anomaly DetectionOutlier Detection	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