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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 201–210 of 227 papers

TitleStatusHype
Efficient Training of One Class Classification-SVMs—0
_p-Norm Multiple Kernel One-Class Fisher Null-Space—0
_p Slack Norm Support Vector Data Description—0
End-to-End Adversarial Learning for Intrusion Detection in Computer Networks—0
Enhancing Sentiment Analysis Results through Outlier Detection Optimization—0
Entropic one-class classifiers—0
Evolutionary Simplicial Learning as a Generative and Compact Sparse Framework for Classification—0
Explainable Artificial Intelligence and Causal Inference based ATM Fraud Detection—0
Exploring the Optimization Objective of One-Class Classification for Anomaly Detection—0
FedDyMem: Efficient Federated Learning with Dynamic Memory and Memory-Reduce for Unsupervised Image Anomaly Detection—0
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