SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 171–180 of 227 papers

TitleStatusHype
A One-Class Classification Decision Tree Based on Kernel Density Estimation—0
Applying support vector data description for fraud detection—0
A Unifying Review of Deep and Shallow Anomaly Detection—0
Automated Image Analysis Framework for the High-Throughput Determination of Grapevine Berry Sizes Using Conditional Random Fields—0
Average Localised Proximity: A new data descriptor with good default one-class classification performance—0
Beyond Generation: A Diffusion-based Low-level Feature Extractor for Detecting AI-generated Images—0
Boosting rare benthic macroinvertebrates taxa identification with one-class classification—0
Chaotic Variational Auto Encoder based One Class Classifier for Insurance Fraud Detection—0
CLEAR: Cumulative LEARning for One-Shot One-Class Image Recognition—0
Combining Lightly-Supervised Text Classification Models for Accurate Contextual Advertising—0
Show:102550
← PrevPage 18 of 23Next →

No leaderboard results yet.