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 311–320 of 703 papers

Title	Date	Tasks	Status
Analyzing categorical time series with the R package ctsfeatures	Apr 24, 2023	ClusteringOutlier Detection	—Unverified
Deep-Anomaly: Fully Convolutional Neural Network for Fast Anomaly Detection in Crowded Scenes	Sep 3, 2016	Anomaly DetectionOutlier Detection	—Unverified
Decision-change Informed Rejection Improves Robustness in Pattern Recognition-based Myoelectric Control	Sep 21, 2024	Outlier Detection	—Unverified
Dealing with Class Imbalance using Thresholding	Jul 10, 2016	ClassificationGeneral Classification	—Unverified
A Robust AUC Maximization Framework with Simultaneous Outlier Detection and Feature Selection for Positive-Unlabeled Classification	Mar 18, 2018	EEGElectroencephalogram (EEG)	—Unverified
Analysis of Learning from Positive and Unlabeled Data	Dec 1, 2014	General ClassificationOutlier Detection	—Unverified
A deep mixture density network for outlier-corrected interpolation of crowd-sourced weather data	Jan 25, 2022	Outlier Detection	—Unverified
A Comprehensive System for Secondary Structure Analysis of Protein Models	Feb 18, 2024	Outlier Detection	—Unverified
3D Labeling Tool	Jul 23, 2022	Objectobject-detection	—Unverified
Data Stream Clustering: A Review	Jul 16, 2020	ClusteringOutlier 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