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 641–650 of 703 papers

Title	Date	Tasks	Status
DOC-NAD: A Hybrid Deep One-class Classifier for Network Anomaly Detection	Dec 15, 2022	Anomaly DetectionIntrusion Detection	—Unverified
Does Your Dermatology Classifier Know What It Doesn't Know? Detecting the Long-Tail of Unseen Conditions	Apr 8, 2021	Outlier DetectionOut of Distribution (OOD) Detection	—Unverified
Do We Really Need to Learn Representations from In-domain Data for Outlier Detection?	May 19, 2021	Outlier DetectionRepresentation Learning	—Unverified
DRGRADUATE: uncertainty-aware deep learning-based diabetic retinopathy grading in eye fundus images	Oct 25, 2019	Diabetic Retinopathy GradingMultiple Instance Learning	—Unverified
ECO-AMLP: A Decision Support System using an Enhanced Class Outlier with Automatic Multilayer Perceptron for Diabetes Prediction	Jun 23, 2017	Diabetes PredictionDisease Prediction	—Unverified
ECORS: An Ensembled Clustering Approach to Eradicate The Local And Global Outlier In Collaborative Filtering Recommender System	Oct 1, 2024	ClusteringCollaborative Filtering	—Unverified
EDoG: Adversarial Edge Detection For Graph Neural Networks	Dec 27, 2022	Drug DesignEdge Detection	—Unverified
Efficient Bregman Range Search	Dec 1, 2009	Density EstimationInformation Retrieval	—Unverified
Efficient Direct Density Ratio Estimation for Non-stationarity Adaptation and Outlier Detection	Dec 1, 2008	Density Ratio EstimationOutlier Detection	—Unverified
Efficient Neural Network based Classification and Outlier Detection for Image Moderation using Compressed Sensing and Group Testing	May 12, 2023	compressed sensingEfficient Neural Network	—Unverified

Show:10 25 50

← PrevPage 65 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