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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 191–200 of 227 papers

TitleStatusHype
One-Class Convolutional Neural NetworkCode1
Generalizing Fingerprint Spoof Detector: Learning a One-Class Classifier—0
Active Learning for One-Class Classification Using Two One-Class Classifiers—0
One-Class Feature Learning Using Intra-Class Splitting—0
OCLEP+: One-class Anomaly and Intrusion Detection Using Minimal Length of Emerging Patterns—0
Feature Learning for Fault Detection in High-Dimensional Condition-Monitoring SignalsCode0
Deep One-Class ClassificationCode0
CLEAR: Cumulative LEARning for One-Shot One-Class Image Recognition—0
Localized Multiple Kernel Learning for Anomaly Detection: One-class ClassificationCode0
Multi-layer Kernel Ridge Regression for One-class Classification—0
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