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 651–660 of 703 papers

Title	Date	Tasks	Status
Unsupervised Skin Lesion Segmentation via Structural Entropy Minimization on Multi-Scale Superpixel Graphs	Sep 5, 2023	Lesion SegmentationOutlier Detection	CodeCode Available
Outlier-Detection for Reactive Machine Learned Potential Energy Surfaces	Feb 27, 2024	Outlier DetectionUncertainty Quantification	CodeCode Available
XGBOD: Improving Supervised Outlier Detection with Unsupervised Representation Learning	Dec 1, 2019	Anomaly DetectionOutlier Detection	CodeCode Available
Outlier Detection for Text Data : An Extended Version	Jan 5, 2017	AttributeOutlier Detection	CodeCode Available
Outlier Detection for Trajectories via Flow-embeddings	Nov 25, 2021	Outlier Detection	CodeCode Available
Sliced-Wasserstein-based Anomaly Detection and Open Dataset for Localized Critical Peak Rebates	Oct 29, 2024	Anomaly DetectionOutlier Detection	CodeCode Available
Sparse Kernel PCA for Outlier Detection	Sep 7, 2018	Outlier Detection	CodeCode Available
Repairing Systematic Outliers by Learning Clean Subspaces in VAEs	Jul 17, 2022	Outlier Detection	CodeCode Available
Anomaly Detection via oversampling Principal Component Analysis	Jul 1, 2013	Anomaly DetectionOutlier Detection	CodeCode Available
Rethinking Unsupervised Outlier Detection via Multiple Thresholding	Jul 7, 2024	Outlier Detection	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