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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 171–180 of 227 papers

TitleStatusHype
One-Class Classification: A Survey—0
One-Class Classification for Intrusion Detection on Vehicular Networks—0
One-Class Classification for Wafer Map using Adversarial Autoencoder with DSVDD Prior—0
One-class Classification Robust to Geometric Transformation—0
One-Class Classification: Taxonomy of Study and Review of Techniques—0
One-Class Feature Learning Using Intra-Class Splitting—0
One-Class Meta-Learning: Towards Generalizable Few-Shot Open-Set Classification—0
One-Class Semi-Supervised Learning: Detecting Linearly Separable Class by its Mean—0
One-Class SVM with Privileged Information and its Application to Malware Detection—0
Online Learning with Regularized Kernel for One-class Classification—0
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