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 101–125 of 227 papers

TitleStatusHype
Combining Lightly-Supervised Text Classification Models for Accurate Contextual Advertising—0
Combining One-Class Classifiers via Meta-Learning—0
Conceptron: a probabilistic deep one-class classification method—0
Conical Classification For Computationally Efficient One-Class Topic Determination—0
Conical Classification For Efficient One-Class Topic Determination—0
Constrained Deep One-Class Feature Learning For Classifying Imbalanced Medical Images—0
Contextual One-Class Classification in Data Streams—0
Point Cloud Novelty Detection Based on Latent Representations of a General Feature Extractor—0
Policy Entropy for Out-of-Distribution Classification—0
Probabilistic Saliency Estimation—0
ProtoFL: Unsupervised Federated Learning via Prototypical Distillation—0
PseudoBound: Limiting the anomaly reconstruction capability of one-class classifiers using pseudo anomalies—0
Quality assurance of organs-at-risk delineation in radiotherapy—0
Refining Myocardial Infarction Detection: A Novel Multi-Modal Composite Kernel Strategy in One-Class Classification—0
Relationship between Variants of One-Class Nearest Neighbours and Creating their Accurate Ensembles—0
[Reproducibility Report] Explainable Deep One-Class Classification—0
Restricted Generative Projection for One-Class Classification and Anomaly Detection—0
Robust Classification of High-Dimensional Spectroscopy Data Using Deep Learning and Data Synthesis—0
Robust One-Class Kernel Spectral Regression—0
SAFE-OCC: A Novelty Detection Framework for Convolutional Neural Network Sensors and its Application in Process Control—0
Score Combining for Contrastive OOD Detection—0
SD-MAD: Sign-Driven Few-shot Multi-Anomaly Detection in Medical Images—0
Self-supervise, Refine, Repeat: Improving Unsupervised Anomaly Detection—0
Semi-supervised Outlier Detection using Generative And Adversary Framework—0
Simple and Effective Prevention of Mode Collapse in Deep One-Class Classification—0
Show:102550
← PrevPage 5 of 10Next →

No leaderboard results yet.