Research Explorer

Project

A combined framework of machine learning and extreme value theory for anomaly detection and ranking
Code
01SC0621
Duration
01 October 2021 → 30 September 2025
Funding
Regional and community funding: Special Research Fund
Research disciplines
  • Natural sciences
    • Statistics not elsewhere classified
    • Machine learning and decision making
Keywords
machine learning extreme value theory anomaly detection high-dimensional data
 
Project description

Anomaly detection is frequently encountered in quantitative research in the life sciences. Conventional machine learning methods are not fully adapted to deal with the identification nor the ranking of rare events. In this PhD track, we aim to combine machine learning methods with principles of extreme value theory, a field in statistics especially suited to build models of extremes.