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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 181–190 of 227 papers

TitleStatusHype
Multi-Task Kernel Null-Space for One-Class Classification—0
A Multi-modal one-class generative adversarial network for anomaly detection in manufacturing—0
End-to-End Adversarial Learning for Intrusion Detection in Computer Networks—0
Multimodal Subspace Support Vector Data DescriptionCode0
Graph-Embedded Multi-layer Kernel Extreme Learning Machine for One-class Classification or (Graph-Embedded Multi-layer Kernel Ridge Regression for One-class Classification)—0
OCKELM+: Kernel Extreme Learning Machine based One-class Classification using Privileged Information (or KOC+: Kernel Ridge Regression or Least Square SVM with zero bias based One-class Classification using Privileged Information)—0
Active Authentication using an Autoencoder regularized CNN-based One-Class ClassifierCode0
Unsupervised Traffic Accident Detection in First-Person VideosCode1
Robust One-Class Kernel Spectral Regression—0
Deep One-Class Classification Using Intra-Class Splitting—0
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