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 561–570 of 703 papers

Title	Date	Tasks	Status
Simple robust genomic prediction and outlier detection for a multi-environmental field trial	Jul 19, 2018	Outlier DetectionPrediction	—Unverified
One-Class Kernel Spectral Regression	Jul 3, 2018	Graph EmbeddingOne-class classifier	—Unverified
Deep One-Class Classification	Jul 1, 2018	Anomaly DetectionClassification	CodeCode Available
A Framework for Developing and Evaluating Word Embeddings of Drug-named Entity	Jul 1, 2018	Named Entity Recognition (NER)Outlier Detection	—Unverified
Model-based Exception Mining for Object-Relational Data	Jul 1, 2018	modelObject	—Unverified
Applications of Data Mining Techniques for Vehicular Ad hoc Networks	Jun 19, 2018	ClassificationClustering	—Unverified
Kernel-based Outlier Detection using the Inverse Christoffel Function	Jun 18, 2018	Outlier Detection	—Unverified
Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection	Jun 13, 2018	Anomaly DetectionDisease Prediction	CodeCode Available
Robust Object Tracking with Crow Search Optimized Multi-cue Particle Filter	Jun 11, 2018	Object TrackingOutlier Detection	—Unverified
A Practical Algorithm for Distributed Clustering and Outlier Detection	May 24, 2018	ClusteringOutlier 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