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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 211–220 of 227 papers

TitleStatusHype
Binary Classification from Positive-Confidence DataCode0
Support Spinor Machine—0
Open-Set Language Identification—0
Model Selection for Anomaly Detection—0
One-Class Semi-Supervised Learning: Detecting Linearly Separable Class by its Mean—0
On The Construction of Extreme Learning Machine for Online and Offline One-Class Classification - An Expanded Toolbox—0
Online Learning with Regularized Kernel for One-class Classification—0
A One class Classifier based Framework using SVDD : Application to an Imbalanced Geological Dataset—0
One Class Splitting Criteria for Random ForestsCode0
Detecting Unseen Falls from Wearable Devices using Channel-wise Ensemble of Autoencoders—0
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