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 271–280 of 703 papers

Title	Date	Tasks	Status
An Evolutionary Game based Secure Clustering Protocol with Fuzzy Trust Evaluation and Outlier Detection for Wireless Sensor Networks	Jul 21, 2022	ClusteringOutlier Detection	—Unverified
A feature construction framework based on outlier detection and discriminative pattern mining	Jul 17, 2014	Dimensionality Reductionfeature selection	—Unverified
Active Relation Discovery: Towards General and Label-aware OpenRE	Nov 16, 2021	Active LearningOutlier Detection	—Unverified
3D Scanning: A Comprehensive Survey	Jan 24, 2018	Outlier DetectionSurvey	—Unverified
Detect Professional Malicious User with Metric Learning in Recommender Systems	May 19, 2022	Metric LearningOutlier Detection	—Unverified
Autoencoder Watchdog Outlier Detection for Classifiers	Oct 24, 2020	Outlier Detectionregression	—Unverified
Detection of Thin Boundaries between Different Types of Anomalies in Outlier Detection using Enhanced Neural Networks	Jan 24, 2020	Anomaly DetectionOutlier Detection	—Unverified
Detection of Peculiar Word Sense by Distance Metric Learning with Labeled Examples	May 1, 2012	General ClassificationMetric Learning	—Unverified
An Evaluation of Classification and Outlier Detection Algorithms	May 2, 2018	ClassificationGeneral Classification	—Unverified
Detection of Abnormal Input-Output Associations	Aug 3, 2017	Outlier DetectionRelation	—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