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 111–120 of 703 papers

Title	Date	Tasks	Status
A Robust Framework for Classifying Evolving Document Streams in an Expert-Machine-Crowd Setting	Oct 6, 2016	ClusteringConstrained Clustering	—Unverified
Automated detection of business-relevant outliers in e-commerce conversion rate	May 15, 2019	Outlier Detection	—Unverified
Component-wise Adaptive Trimming For Robust Mixture Regression	May 23, 2020	Outlier Detectionparameter estimation	—Unverified
A feature construction framework based on outlier detection and discriminative pattern mining	Jul 17, 2014	Dimensionality Reductionfeature selection	—Unverified
Automatically Identifying Pseudepigraphic Texts	Oct 1, 2013	Outlier Detection	—Unverified
Automatic Outlier Rectification via Optimal Transport	Mar 21, 2024	Outlier Detection	—Unverified
An Improved Heart Disease Prediction Using Stacked Ensemble Method	Apr 12, 2023	DiagnosticDisease Prediction	—Unverified
Automatic Unsupervised Outlier Model Selection	Dec 1, 2021	Meta-Learningmodel	—Unverified
Analyzing categorical time series with the R package ctsfeatures	Apr 24, 2023	ClusteringOutlier Detection	—Unverified
A Robust AUC Maximization Framework with Simultaneous Outlier Detection and Feature Selection for Positive-Unlabeled Classification	Mar 18, 2018	EEGElectroencephalogram (EEG)	—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