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 341–350 of 703 papers

Title	Date	Tasks	Status
A deep mixture density network for outlier-corrected interpolation of crowd-sourced weather data	Jan 25, 2022	Outlier Detection	—Unverified
Adaptive Outlier Detection for Power MOSFETs Based on Gaussian Process Regression	Jan 25, 2022	GPROutlier Detection	—Unverified
Community-based anomaly detection using spectral graph filtering	Jan 24, 2022	Anomaly DetectionOutlier Detection	—Unverified
An Efficient Hashing-based Ensemble Method for Collaborative Outlier Detection	Jan 18, 2022	Outlier Detection	—Unverified
Anomaly Detection using Capsule Networks for High-dimensional Datasets	Dec 27, 2021	Anomaly DetectionBinary Classification	—Unverified
Understanding and Mitigating the Effect of Outliers in Fair Ranking	Dec 21, 2021	FairnessOutlier Detection	CodeCode Available
Federated Nearest Neighbor Classification with a Colony of Fruit-Flies: With Supplement	Dec 14, 2021	Federated LearningOutlier Detection	CodeCode Available
Robust factored principal component analysis for matrix-valued outlier accommodation and detection	Dec 13, 2021	Dimensionality ReductionOutlier Detection	—Unverified
Automatic Unsupervised Outlier Model Selection	Dec 1, 2021	Meta-Learningmodel	—Unverified
Outlier Detection using AI: A Survey	Dec 1, 2021	Outlier DetectionSurvey	—Unverified

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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