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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 41–50 of 227 papers

TitleStatusHype
Applying support vector data description for fraud detection—0
A Joint Representation Learning and Feature Modeling Approach for One-class Recognition—0
A One-Class Classification Decision Tree Based on Kernel Density Estimation—0
Data refinement for fully unsupervised visual inspection using pre-trained networks—0
A One class Classifier based Framework using SVDD : Application to an Imbalanced Geological Dataset—0
Abuse and Fraud Detection in Streaming Services Using Heuristic-Aware Machine Learning—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
Critical Review for One-class Classification: recent advances and the reality behind them—0
Deep Inverse Reinforcement Learning via Adversarial One-Class Classification—0
Credit Card Fraud Detection with Subspace Learning-based One-Class Classification—0
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