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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 211–220 of 227 papers

TitleStatusHype
Feature Learning for Fault Detection in High-Dimensional Condition-Monitoring SignalsCode0
One Class Restricted Kernel MachinesCode0
Adversarially Learned One-Class Classifier for Novelty DetectionCode0
One Class Splitting Criteria for Random ForestsCode0
Quantum One-class Classification With a Distance-based ClassifierCode0
Ellipsoidal Subspace Support Vector Data DescriptionCode0
Multimodal Subspace Support Vector Data DescriptionCode0
Query by example in remote sensing image archive using enhanced deep support vector data descriptionCode0
Multi-view Deep One-class Classification: A Systematic ExplorationCode0
Image Outlier Detection Without Training using RANSACCode0
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