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 381–390 of 703 papers

Title	Date	Tasks	Status
Outlier Detection and Spatial Analysis Algorithms	Jun 20, 2021	Outlier Detection	—Unverified
Comparison of Outlier Detection Techniques for Structured Data	Jun 16, 2021	BIG-bench Machine LearningOutlier Detection	—Unverified
Out-of-Scope Intent Detection with Self-Supervision and Discriminative Training	Jun 16, 2021	Intent DetectionOutlier Detection	CodeCode Available
Partial Wasserstein and Maximum Mean Discrepancy distances for bridging the gap between outlier detection and drift detection	Jun 9, 2021	Drift DetectionNovelty Detection	CodeCode Available
Safeguarding against spurious AI-based predictions: The case of automated verbal memory assessment	Jun 1, 2021	AttributeInductive Bias	—Unverified
Weighting vectors for machine learning: numerical harmonic analysis applied to boundary detection	Jun 1, 2021	BIG-bench Machine LearningBoundary Detection	—Unverified
Do We Really Need to Learn Representations from In-domain Data for Outlier Detection?	May 19, 2021	Outlier DetectionRepresentation Learning	—Unverified
Towards a Model for LSH	May 11, 2021	Clusteringmodel	—Unverified
Novelty Detection and Analysis of Traffic Scenario Infrastructures in the Latent Space of a Vision Transformer-Based Triplet Autoencoder	May 5, 2021	Autonomous VehiclesNovelty Detection	CodeCode Available
ReLearn: A Robust Machine Learning Framework in Presence of Missing Data for Multimodal Stress Detection from Physiological Signals	Apr 29, 2021	BIG-bench Machine Learningfeature selection	—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