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 291–300 of 703 papers

Title	Date	Tasks	Status
A Study of Deep Learning for Network Traffic Data Forecasting	Sep 10, 2019	ClusteringDeep Learning	—Unverified
An Efficient Outlier Detection Algorithm for Data Streaming	Jan 2, 2025	Anomaly DetectionFraud Detection	—Unverified
Detecting abnormal events in video using Narrowed Normality Clusters	Jan 12, 2018	Anomaly DetectionClustering	—Unverified
Dense outlier detection and open-set recognition based on training with noisy negative images	Jan 22, 2021	Open Set LearningOutlier Detection	—Unverified
A specifically designed machine learning algorithm for GNSS position time series prediction and its applications in outlier and anomaly detection and earthquake prediction	Jun 16, 2020	Anomaly DetectionBIG-bench Machine Learning	—Unverified
An Efficient Hashing-based Ensemble Method for Collaborative Outlier Detection	Jan 18, 2022	Outlier Detection	—Unverified
Active Learning of SVDD Hyperparameter Values	Dec 4, 2019	Active LearningOutlier Detection	—Unverified
DEK-Forecaster: A Novel Deep Learning Model Integrated with EMD-KNN for Traffic Prediction	Jun 6, 2023	Decision MakingDenoising	—Unverified
Toward Scalable and Unified Example-based Explanation and Outlier Detection	Nov 11, 2020	Decision MakingOutlier Detection	—Unverified
Deep Variational Semi-Supervised Novelty Detection	Nov 12, 2019	Anomaly DetectionAstronomy	—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