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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 111–120 of 227 papers

TitleStatusHype
_p Slack Norm Support Vector Data Description—0
Improving State-of-the-Art in One-Class Classification by Leveraging Unlabeled DataCode0
Generative Cooperative Learning for Unsupervised Video Anomaly Detection—0
Abuse and Fraud Detection in Streaming Services Using Heuristic-Aware Machine Learning—0
Mobile authentication of copy detection patterns—0
Towards Targeted Change Detection with Heterogeneous Remote Sensing Images for Forest Mortality Mapping—0
Data refinement for fully unsupervised visual inspection using pre-trained networks—0
On The Effectiveness of One-Class Support Vector Machine in Different Defect Prediction Scenarios—0
SAFE-OCC: A Novelty Detection Framework for Convolutional Neural Network Sensors and its Application in Process Control—0
FROB: Few-shot ROBust Model for Classification and Out-of-Distribution Detection—0
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