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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 71–80 of 227 papers

TitleStatusHype
Newton Method-based Subspace Support Vector Data Description—0
One-Class Classification for Intrusion Detection on Vehicular Networks—0
Convolutional autoencoder-based multimodal one-class classification—0
Active anomaly detection based on deep one-class classification—0
An Iterative Method for Unsupervised Robust Anomaly Detection Under Data Contamination—0
A Perceptron-based Fine Approximation Technique for Linear Separation—0
CA2: Class-Agnostic Adaptive Feature Adaptation for One-class ClassificationCode0
Anomaly detection with semi-supervised classification based on risk estimators—0
Exploring the Optimization Objective of One-Class Classification for Anomaly Detection—0
AMAE: Adaptation of Pre-Trained Masked Autoencoder for Dual-Distribution Anomaly Detection in Chest X-Rays—0
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