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 621–630 of 703 papers

Title	Date	Tasks	Status
REMIX: Automated Exploration for Interactive Outlier Detection	May 17, 2017	Meta-LearningOutlier Detection	—Unverified
Provable Self-Representation Based Outlier Detection in a Union of Subspaces	Apr 12, 2017	Outlier Detection	—Unverified
Word Embeddings via Tensor Factorization	Apr 10, 2017	Outlier DetectionWord Embeddings	CodeCode Available
Efficient variational Bayesian neural network ensembles for outlier detection	Mar 20, 2017	Outlier Detection	CodeCode Available
Outlier Cluster Formation in Spectral Clustering	Mar 3, 2017	ClusteringFace Clustering	—Unverified
Interpreting Outliers: Localized Logistic Regression for Density Ratio Estimation	Feb 21, 2017	Density Ratio EstimationOutlier Detection	—Unverified
Outlier Detection for Text Data : An Extended Version	Jan 5, 2017	AttributeOutlier Detection	CodeCode Available
Meta-Unsupervised-Learning: A supervised approach to unsupervised learning	Dec 29, 2016	ClusteringDecision Making	—Unverified
Detecting Unusual Input-Output Associations in Multivariate Conditional Data	Dec 21, 2016	Outlier Detection	—Unverified
A General Framework for Density Based Time Series Clustering Exploiting a Novel Admissible Pruning Strategy	Dec 2, 2016	AstronomyClustering	—Unverified

Show:10 25 50

← PrevPage 63 of 71Next →

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