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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 61–70 of 227 papers

TitleStatusHype
Lp-Norm Constrained One-Class Classifier Combination—0
Advancing Image Retrieval with Few-Shot Learning and Relevance FeedbackCode0
Learning Polynomial Representations of Physical Objects with Application to Certifying Correct Packing Configurations—0
OCGEC: One-class Graph Embedding Classification for DNN Backdoor DetectionCode0
Video Anomaly Detection via Spatio-Temporal Pseudo-Anomaly Generation : A Unified Approach—0
Enhancing Sentiment Analysis Results through Outlier Detection Optimization—0
Interpretable pap smear cell representation for cervical cancer screening—0
Deep Learning Predicts Biomarker Status and Discovers Related Histomorphology Characteristics for Low-Grade Glioma—0
Efficient Training of One Class Classification-SVMs—0
Credit Card Fraud Detection with Subspace Learning-based One-Class Classification—0
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