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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 201–210 of 227 papers

TitleStatusHype
MEATRD: Multimodal Anomalous Tissue Region Detection Enhanced with Spatial TranscriptomicsCode0
CA2: Class-Agnostic Adaptive Feature Adaptation for One-class ClassificationCode0
Graph-Embedded Subspace Support Vector Data DescriptionCode0
Meta-Learning for One-Class Classification with Few Examples using Order-Equivariant NetworkCode0
RoCA: Robust Contrastive One-class Time Series Anomaly Detection with Contaminated DataCode0
Detecting Adversarial Examples for Speech Recognition via Uncertainty QuantificationCode0
Metre as a stylometric feature in Latin hexameter poetryCode0
Adversarial Subspace Generation for Outlier Detection in High-Dimensional DataCode0
Advancing Image Retrieval with Few-Shot Learning and Relevance FeedbackCode0
Generalized Reference Kernel for One-class ClassificationCode0
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