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 371–380 of 703 papers

Title	Date	Tasks	Status
Towards a Model for LSH	May 11, 2021	Clusteringmodel	—Unverified
Towards an Ensemble Regressor Model for Anomalous ISP Traffic Prediction	May 3, 2022	ManagementOutlier Detection	—Unverified
Towards Auditing Unsupervised Learning Algorithms and Human Processes For Fairness	Sep 20, 2022	ClassificationClustering	—Unverified
Towards Building Affect sensitive Word Distributions	Jan 1, 2018	Outlier DetectionWord Similarity	—Unverified
Towards Reliable Zero Shot Classification in Self-Supervised Models with Conformal Prediction	Oct 27, 2022	ClassificationConformal Prediction	—Unverified
Hyperparameter Optimization for Unsupervised Outlier Detection	Aug 24, 2022	Hyperparameter OptimizationMeta-Learning	—Unverified
Traffic congestion anomaly detection and prediction using deep learning	Jun 23, 2020	Anomaly DetectionDeep Learning	—Unverified
Transfer Neyman-Pearson Algorithm for Outlier Detection	Jan 2, 2025	Classificationimbalanced classification	—Unverified
Transformation Autoregressive Networks	Jan 30, 2018	Density EstimationOutlier Detection	—Unverified
TRIDIS: A Comprehensive Medieval and Early Modern Corpus for HTR and NER	Mar 25, 2025	Handwritten Text RecognitionHTR	—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