SOTAVerified

One-Class Classification

One-class classification (OCC) algorithms serve a crucial role in scenarios where the negative class is either absent, poorly sampled, or not well defined. This unique situation presents a challenge for building effective classifiers, as they must delineate the class boundary solely based on knowledge of the positive class. OCC has found application in various research domains, including outlier/novelty detection and concept learning.

In the context of anomaly detection, OCC models are trained exclusively on "normal" data and are subsequently tasked with identifying anomalous patterns during inference.

A one-class classifier aims at capturing characteristics of training instances, in order to be able to distinguish between them and potential outliers to appear.

— Page 139, Learning from Imbalanced Data Sets, 2018.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 81–90 of 227 papers

TitleStatusHype
One-Class Risk Estimation for One-Class Hyperspectral Image ClassificationCode1
Learning The Likelihood Test With One-Class Classifiers for Physical Layer Authentication—0
Self-Supervised Masked Convolutional Transformer Block for Anomaly DetectionCode1
A One-Class Classification method based on Expanded Non-Convex HullsCode0
Calibrated One-class Classification for Unsupervised Time Series Anomaly DetectionCode1
Hierarchical Semi-Supervised Contrastive Learning for Contamination-Resistant Anomaly DetectionCode0
Deep Contrastive One-Class Time Series Anomaly DetectionCode1
Dual-Distribution Discrepancy for Anomaly Detection in Chest X-RaysCode1
[Reproducibility Report] Explainable Deep One-Class Classification—0
Generalized Reference Kernel for One-class ClassificationCode0
Show:102550
← PrevPage 9 of 23Next →

No leaderboard results yet.