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 251–260 of 703 papers

Title	Date	Tasks	Status
A system for exploring big data: an iterative k-means searchlight for outlier detection on open health data	Apr 5, 2023	Outlier Detection	—Unverified
MSS-PAE: Saving Autoencoder-based Outlier Detection from Unexpected Reconstruction	Apr 3, 2023	Outlier Detection	CodeCode Available
OutCenTR: A novel semi-supervised framework for predicting exploits of vulnerabilities in high-dimensional datasets	Apr 3, 2023	Dimensionality ReductionOutlier Detection	—Unverified
GAN-RXA: A Practical Scalable Solution to Receiver-Agnostic Transmitter Fingerprinting	Mar 25, 2023	Outlier Detection	—Unverified
GADformer: A Transparent Transformer Model for Group Anomaly Detection on Trajectories	Mar 17, 2023	Anomaly DetectionGroup Anomaly Detection	CodeCode Available
RODD: Robust Outlier Detection in Data Cubes	Mar 14, 2023	Outlier Detection	—Unverified
Interpretable Outlier Summarization	Mar 11, 2023	Anomaly DetectionOutlier Detection	—Unverified
AnoMalNet: Outlier Detection based Malaria Cell Image Classification Method Leveraging Deep Autoencoder	Mar 10, 2023	image-classificationImage Classification	—Unverified
Image Labels Are All You Need for Coarse Seagrass Segmentation	Mar 2, 2023	AllOutlier Detection	CodeCode Available
Proof-of-Contribution-Based Design for Collaborative Machine Learning on Blockchain	Feb 27, 2023	Contribution AssessmentFederated Learning	—Unverified

Show:10 25 50

← PrevPage 26 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