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 361–370 of 703 papers

Title	Date	Tasks	Status
PyODDS: An End-to-end Outlier Detection System with Automated Machine Learning	Mar 12, 2020	BIG-bench Machine LearningOutlier Detection	—Unverified
Quantifying Outlierness of Funds from their Categories using Supervised Similarity	Aug 14, 2023	ManagementMetric Learning	—Unverified
Radio Galaxy Zoo: Unsupervised Clustering of Convolutionally Auto-encoded Radio-astronomical Images	Jun 7, 2019	BIG-bench Machine LearningClustering	—Unverified
Random Subspace Learning Approach to High-Dimensional Outliers Detection	Feb 16, 2015	Outlier DetectionVocal Bursts Intensity Prediction	—Unverified
RANRAC: Robust Neural Scene Representations via Random Ray Consensus	Dec 15, 2023	Novel View SynthesisOutlier Detection	—Unverified
Rapid Distance-Based Outlier Detection via Sampling	Dec 1, 2013	Outlier Detection	—Unverified
RCC-Dual-GAN: An Efficient Approach for Outlier Detection with Few Identified Anomalies	Mar 7, 2020	Outlier Detection	—Unverified
Real-Time Outlier Detection with Dynamic Process Limits	Jan 31, 2023	Anomaly DetectionOutlier Detection	—Unverified
Real-time Wireless Transmitter Authorization: Adapting to Dynamic Authorized Sets with Information Retrieval	Nov 4, 2021	Deep LearningDimensionality Reduction	—Unverified
RECol: Reconstruction Error Columns for Outlier Detection	Feb 4, 2021	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