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 91–100 of 703 papers

Title	Date	Tasks	Status
Consensus Clustering: An Embedding Perspective, Extension and Beyond	May 31, 2019	ClusteringConstrained Clustering	—Unverified
An Efficient Outlier Detection Algorithm for Data Streaming	Jan 2, 2025	Anomaly DetectionFraud Detection	—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
AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning	Jun 19, 2020	Fraud Detectionimage-classification	—Unverified
An Approximate Bayesian Long Short-Term Memory Algorithm for Outlier Detection	Dec 23, 2017	Outlier Detection	—Unverified
A Robust Regression Approach for Robot Model Learning	Aug 23, 2019	modelOutlier Detection	—Unverified
A Comprehensive System for Secondary Structure Analysis of Protein Models	Feb 18, 2024	Outlier Detection	—Unverified
3D Labeling Tool	Jul 23, 2022	Objectobject-detection	—Unverified
A Robust Learning Algorithm for Regression Models Using Distributionally Robust Optimization under the Wasserstein Metric	Jun 7, 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