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 551–560 of 703 papers

Title	Date	Tasks	Status
A specifically designed machine learning algorithm for GNSS position time series prediction and its applications in outlier and anomaly detection and earthquake prediction	Jun 16, 2020	Anomaly DetectionBIG-bench Machine Learning	—Unverified
A Study of Deep Learning for Network Traffic Data Forecasting	Sep 10, 2019	ClusteringDeep Learning	—Unverified
A system for exploring big data: an iterative k-means searchlight for outlier detection on open health data	Apr 5, 2023	Outlier Detection	—Unverified
Attack Strength vs. Detectability Dilemma in Adversarial Machine Learning	Feb 20, 2018	BIG-bench Machine Learningfeature selection	—Unverified
A Unified Framework for Center-based Clustering of Distributed Data	Feb 2, 2024	ClusteringOutlier Detection	—Unverified
Autoencoder Watchdog Outlier Detection for Classifiers	Oct 24, 2020	Outlier Detectionregression	—Unverified
Automated detection of business-relevant outliers in e-commerce conversion rate	May 15, 2019	Outlier Detection	—Unverified
Automatically Identifying Pseudepigraphic Texts	Oct 1, 2013	Outlier Detection	—Unverified
Automatic Outlier Rectification via Optimal Transport	Mar 21, 2024	Outlier Detection	—Unverified
Automatic Unsupervised Outlier Model Selection	Dec 1, 2021	Meta-Learningmodel	—Unverified

Show:10 25 50

← PrevPage 56 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