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 511–520 of 703 papers

Title	Date	Tasks	Status
Embedding-Based Complex Feature Value Coupling Learning for Detecting Outliers in Non-IID Categorical Data	Jul 1, 2019	Outlier Detection	CodeCode Available
Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty	Jun 28, 2019	Anomaly DetectionOutlier Detection	CodeCode Available
Quantum Entropy Scoring for Fast Robust Mean Estimation and Improved Outlier Detection	Jun 26, 2019	Outlier Detection	CodeCode Available
AI-enabled Blockchain: An Outlier-aware Consensus Protocol for Blockchain-based IoT Networks	Jun 17, 2019	BIG-bench Machine LearningOutlier Detection	—Unverified
Anomaly Detection with HMM Gauge Likelihood Analysis	Jun 14, 2019	Anomaly DetectionClustering	—Unverified
Radio Galaxy Zoo: Unsupervised Clustering of Convolutionally Auto-encoded Radio-astronomical Images	Jun 7, 2019	BIG-bench Machine LearningClustering	—Unverified
Nonconvex Approach for Sparse and Low-Rank Constrained Models with Dual Momentum	Jun 6, 2019	ClusteringDenoising	—Unverified
Deep Semi-Supervised Anomaly Detection	Jun 6, 2019	Anomaly DetectionOutlier Detection	CodeCode Available
MaxGap Bandit: Adaptive Algorithms for Approximate Ranking	Jun 3, 2019	Outlier Detection	CodeCode Available
Consensus Clustering: An Embedding Perspective, Extension and Beyond	May 31, 2019	ClusteringConstrained Clustering	—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