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 131–140 of 703 papers

Title	Date	Tasks	Status
A Review of Graph-Powered Data Quality Applications for IoT Monitoring Sensor Networks	Oct 28, 2024	ImputationOutlier Detection	—Unverified
Lifted Coefficient of Determination: Fast model-free prediction intervals and likelihood-free model comparison	Oct 11, 2024	modelOutlier Detection	—Unverified
ECORS: An Ensembled Clustering Approach to Eradicate The Local And Global Outlier In Collaborative Filtering Recommender System	Oct 1, 2024	ClusteringCollaborative Filtering	—Unverified
Conditional Testing based on Localized Conformal p-values	Sep 25, 2024	Outlier DetectionPrediction Intervals	—Unverified
Decision-change Informed Rejection Improves Robustness in Pattern Recognition-based Myoelectric Control	Sep 21, 2024	Outlier Detection	—Unverified
Outlier Detection with Cluster Catch Digraphs	Sep 17, 2024	ClusteringOutlier Detection	—Unverified
Zero-shot Outlier Detection via Prior-data Fitted Networks: Model Selection Bygone!	Sep 9, 2024	Model SelectionOutlier Detection	—Unverified
Interpreting Outliers in Time Series Data through Decoding Autoencoder	Sep 3, 2024	Anomaly DetectionExplainable artificial intelligence	—Unverified
Synthetic Data Generation and Automated Multidimensional Data Labeling for AI/ML in General and Circular Coordinates	Sep 3, 2024	Outlier DetectionSynthetic Data Generation	—Unverified
Robust Statistical Scaling of Outlier Scores: Improving the Quality of Outlier Probabilities for Outliers (Extended Version)	Aug 28, 2024	Outlier Detection	—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