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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

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Showing 121–130 of 227 papers

TitleStatusHype
Self-supervise, Refine, Repeat: Improving Unsupervised Anomaly Detection—0
Deep Visual Anomaly detection with Negative Learning—0
DOC3-Deep One Class Classification using Contradictions—0
Graph-Embedded Subspace Support Vector Data DescriptionCode0
Multi-view Deep One-class Classification: A Systematic ExplorationCode0
Federated Learning-based Active Authentication on Mobile Devices—0
One-class Autoencoder Approach for Optimal Electrode Set-up Identification in Wearable EEG Event Monitoring—0
Meta-learning One-class Classifiers with Eigenvalue Solvers for Supervised Anomaly Detection—0
Optimised one-class classification performance—0
Average Localised Proximity: A new data descriptor with good default one-class classification performance—0
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