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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
Implicit supervision for fault detection and segmentation of emerging fault types with Deep Variational Autoencoders—0
One-Class Classification by Ensembles of Regression models -- a detailed studyCode0
NFAD: Fixing anomaly detection using normalizing flowsCode0
Metre as a stylometric feature in Latin hexameter poetryCode0
Host-based anomaly detection using Eigentraces feature extraction and one-class classification on system call trace data—0
Statistical and Machine Learning-based Decision Techniques for Physical Layer Authentication—0
Domain Adaptation for One-Class Classification: Monitoring the Health of Critical Systems Under Limited Information—0
Contextual One-Class Classification in Data Streams—0
Self-Attentive, Multi-Context One-Class Classification for Unsupervised Anomaly Detection on TextCode0
Domain Adaptive Attention Learning for Unsupervised Person Re-Identification—0
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