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 321–330 of 703 papers

Title	Date	Tasks	Status
Towards an Ensemble Regressor Model for Anomalous ISP Traffic Prediction	May 3, 2022	ManagementOutlier Detection	—Unverified
Improving the Robustness of Federated Learning for Severely Imbalanced Datasets	Apr 28, 2022	Federated Learningimbalanced classification	—Unverified
Performance Analysis of Out-of-Distribution Detection on Trained Neural Networks	Apr 26, 2022	Outlier DetectionOut-of-Distribution Detection	—Unverified
Capturing the Denoising Effect of PCA via Compression Ratio	Apr 22, 2022	ClusteringDenoising	—Unverified
Fluctuation-based Outlier Detection	Apr 21, 2022	Outlier Detection	CodeCode Available
Learning to Classify Open Intent via Soft Labeling and Manifold Mixup	Apr 16, 2022	intent-classificationIntent Classification	CodeCode Available
Anomalous Sound Detection Based on Machine Activity Detection	Apr 15, 2022	Action DetectionActivity Detection	—Unverified
Robust and Explainable Autoencoders for Unsupervised Time Series Outlier Detection---Extended Version	Apr 7, 2022	Outlier DetectionTime Series	—Unverified
A multi-stream deep neural network with late fuzzy fusion for real-world anomaly detection	Mar 27, 2022	Abnormal Event Detection In VideoAnomaly Detection	—Unverified
ALDI++: Automatic and parameter-less discord and outlier detection for building energy load profiles	Mar 13, 2022	BenchmarkingBIG-bench Machine Learning	CodeCode Available

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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