One-Class Classification
60 papers with code • 0 benchmarks • 0 datasets
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.
Benchmarks
These leaderboards are used to track progress in One-Class Classification
Most implemented papers
Binary Classification from Positive-Confidence Data
takashiishida/pconf
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NeurIPS 2018
Can we learn a binary classifier from only positive data, without any negative data or unlabeled data?
Subspace Support Vector Data Description
The method iteratively optimizes the data mapping along with data description in order to define a compact class representation in a low-dimensional feature space.
One-Class Adversarial Nets for Fraud Detection
PanpanZheng/OCAN
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Currently, most of the fraud detection approaches require a training dataset that contains records of both benign and malicious users.
Localized Multiple Kernel Learning for Anomaly Detection: One-class Classification
In this paper, we present a multiple kernel learning approach for the One-class Classification (OCC) task and employ it for anomaly detection.
Deep One-Class Classification
lukasruff/Deep-SVDD-PyTorch
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ICML 2018
Despite the great advances made by deep learning in many machine learning problems, there is a relative dearth of deep learning approaches for anomaly detection.
Feature Learning for Fault Detection in High-Dimensional Condition-Monitoring Signals
The performance is first evaluated on a synthetic dataset that encompasses typical characteristics of condition monitoring data.
Multimodal Subspace Support Vector Data Description
In this paper, we propose a novel method for projecting data from multiple modalities to a new subspace optimized for one-class classification.
Self-Attentive, Multi-Context One-Class Classification for Unsupervised Anomaly Detection on Text
lukasruff/CVDD-PyTorch
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ACL 2019
There exist few text-specific methods for unsupervised anomaly detection, and for those that do exist, none utilize pre-trained models for distributed vector representations of words.
Metre as a stylometric feature in Latin hexameter poetry
This paper demonstrates that metre is a privileged indicator of authorial style in classical Latin hexameter poetry.
NFAD: Fixing anomaly detection using normalizing flows
lambda-hse/nfad
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Most of the conventional approaches to anomaly detection, such as one-class SVM and Robust Auto-Encoder, are one-class classification methods, i. e. focus on separating normal data from the rest of the space.
