← Back to papers
2020 JMLR JMLR 2020

(1 + epsilon)-class Classification: an Anomaly Detection Method for Highly Imbalanced or Incomplete Data Sets

Abstract

Anomaly detection is not an easy problem since distribution of anomalous samples is unknown a priori. We explore a novel method that gives a trade-off possibility between one-class and two-class approaches, and leads to a better performance on anomaly detection problems with small or non-representative anomalous samples. The method is evaluated using several data sets and compared to a set of conventional one-class and two-class approaches. [abs] [ pdf ][ bib ] [ code ] © JMLR 2020. (edit, beta)

🌉 Interdisciplinary Bridge — Computer Vision and Machine Learning
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Maxim Borisyak , Artem Ryzhikov , Andrey Ustyuzhanin , Denis Derkach , Fedor Ratnikov , Olga Mineeva

Topics

Machine Learning > Core Methods > Classification Computer Vision > Analysis > Anomaly Detection

Keywords

anomaly detection support vector machine imbalanced datum one-class classification two-class classification
Download PDF

Related papers

Weighted Message Passing and Minimum Energy Flow for Heterogeneous Stochastic Block Models with Side Information 2020
Connecting Spectral Clustering to Maximum Margins and Level Sets 2020
Stochastic Nested Variance Reduction for Nonconvex Optimization 2020
Gradient Descent for Sparse Rank-One Matrix Completion for Crowd-Sourced Aggregation of Sparsely Interacting Workers 2020
A Sparse Semismooth Newton Based Proximal Majorization-Minimization Algorithm for Nonconvex Square-Root-Loss Regression Problems 2020