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 551–560 of 703 papers

Title	Date	Tasks	Status
Efficient variational Bayesian neural network ensembles for outlier detection	Mar 20, 2017	Outlier Detection	CodeCode Available
Elastic Similarity and Distance Measures for Multivariate Time Series	Feb 20, 2021	ClassificationDynamic Time Warping	CodeCode Available
Meta-survey on outlier and anomaly detection	Dec 12, 2023	Anomaly DetectionBenchmarking	CodeCode Available
Embedding-Based Complex Feature Value Coupling Learning for Detecting Outliers in Non-IID Categorical Data	Jul 1, 2019	Outlier Detection	CodeCode Available
A Fast Greedy Algorithm for Outlier Mining	Apr 1, 2006	Outlier Detection	CodeCode Available
Efficient Curation of Invertebrate Image Datasets Using Feature Embeddings and Automatic Size Comparison	Dec 20, 2024	Outlier Detection	CodeCode Available
MIMIC-Extract: A Data Extraction, Preprocessing, and Representation Pipeline for MIMIC-III	Jul 19, 2019	BIG-bench Machine LearningLength-of-Stay prediction	CodeCode Available
Enhancing Diversity in Bayesian Deep Learning via Hyperspherical Energy Minimization of CKA	Oct 31, 2024	Deep LearningDiversity	CodeCode Available
A Radiometric Correction based Optical Modeling Approach to Removing Reflection Noise in TLS Point Clouds of Urban Scenes	Jul 3, 2024	3D ReconstructionAutonomous Driving	CodeCode Available
MIX: A Joint Learning Framework for Detecting Both Clustered and Scattered Outliers in Mixed-Type Data	Nov 1, 2019	Anomaly DetectionOutlier Detection	CodeCode Available

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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