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 471–480 of 703 papers

Title	Date	Tasks	Status
IPOF: An Extremely and Excitingly Simple Outlier Detection Booster via Infinite Propagation	Aug 1, 2021	Outlier Detection	—Unverified
Irregularity Detection in Categorized Document Corpora	May 1, 2012	ArticlesDocument Classification	—Unverified
Isolation forests: looking beyond tree depth	Nov 23, 2021	Outlier Detection	—Unverified
It Is Likely That Your Loss Should be a Likelihood	Jul 12, 2020	Outlier Detection	—Unverified
JANUS: Benchmarking Commercial and Open-Source Cloud and Edge Platforms for Object and Anomaly Detection Workloads	Dec 9, 2020	Anomaly DetectionBenchmarking	—Unverified
K-ARMA Models for Clustering Time Series Data	Jun 30, 2022	ClusteringOutlier Detection	—Unverified
Kernel Bandwidth Selection for SVDD: Peak Criterion Approach for Large Data	Oct 31, 2016	Outlier Detection	—Unverified
Kernel-based Outlier Detection using the Inverse Christoffel Function	Jun 18, 2018	Outlier Detection	—Unverified
Kernel Random Projection Depth for Outlier Detection	Jun 12, 2023	Outlier Detection	—Unverified
KNN Ensembles for Tweedie Regression: The Power of Multiscale Neighborhoods	Jul 29, 2017	Outlier Detectionregression	—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