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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 141–150 of 227 papers

TitleStatusHype
Understanding Time Series Anomaly State Detection through One-Class Classification—0
Unsupervised Artifact Detection for Whole Slide Images of Prostate Biopsies—0
Unsupervised Deep One-Class Classification with Adaptive Threshold based on Training Dynamics—0
Unsupervised Learning of the Set of Local Maxima—0
Use of in-the-wild images for anomaly detection in face anti-spoofing—0
usfAD Based Effective Unknown Attack Detection Focused IDS Framework—0
Video Anomaly Detection via Spatio-Temporal Pseudo-Anomaly Generation : A Unified Approach—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
Learning Off-Road Terrain Traversability with Self-Supervisions Only—0
Learning Polynomial Representations of Physical Objects with Application to Certifying Correct Packing Configurations—0
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