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 361–370 of 703 papers

Title	Date	Tasks	Status
BAHP: Benchmark of Assessing Word Embeddings in Historical Portuguese	Nov 1, 2021	Outlier DetectionWord Embeddings	—Unverified
The magnitude vector of images	Oct 28, 2021	Boundary DetectionDescriptive	CodeCode Available
Anomaly-Injected Deep Support Vector Data Description for Text Outlier Detection	Oct 27, 2021	Anomaly DetectionOutlier Detection	—Unverified
News-based Business Sentiment and its Properties as an Economic Index	Oct 20, 2021	ArticlesOutlier Detection	—Unverified
Large Scale Substitution-based Word Sense Induction	Oct 14, 2021	Outlier DetectionWord Embeddings	—Unverified
C-AllOut: Catching & Calling Outliers by Type	Oct 13, 2021	Outlier DetectionTAG	—Unverified
Distance Based Pattern Driven Mining for Outlier Detection in High Dimensional Big Dataset	Oct 5, 2021	Outlier Detection	—Unverified
Probabilistic Robust Autoencoders for Outlier Detection	Oct 1, 2021	Anomaly DetectionOutlier Detection	—Unverified
When Complexity Is Good: Do We Need Recurrent Deep Learning For Time Series Outlier Detection?	Sep 29, 2021	Home Activity MonitoringOutlier Detection	—Unverified
Learning to Rank Anomalies: Scalar Performance Criteria and Maximization of Two-Sample Rank Statistics	Sep 20, 2021	Binary ClassificationFraud Detection	—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