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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 101–110 of 227 papers

TitleStatusHype
Evolutionary Simplicial Learning as a Generative and Compact Sparse Framework for Classification—0
Entropic one-class classifiers—0
Interpretable pap smear cell representation for cervical cancer screening—0
CLEAR: Cumulative LEARning for One-Shot One-Class Image Recognition—0
On The Effectiveness of One-Class Support Vector Machine in Different Defect Prediction Scenarios—0
Implicit supervision for fault detection and segmentation of emerging fault types with Deep Variational Autoencoders—0
Learning Off-Road Terrain Traversability with Self-Supervisions Only—0
Deep Visual Anomaly detection with Negative Learning—0
Enhancing Sentiment Analysis Results through Outlier Detection Optimization—0
End-to-End Adversarial Learning for Intrusion Detection in Computer Networks—0
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