An incremental machine learning algorithm for nuclear forensics

Abstract

This paper presents an incremental machine learning algorithm that identifies the origin, or provenance, of samples of nuclear material. This is part of work being undertaken by the Canadian National Nuclear Forensics Library development program, which seeks to build a comprehensive database of signatures for radioactive and nuclear materials under Canadian regulatory control. One difficulty with this application is the small ratio of the number of examples over the number of classes. So, we introduce variants to a basic generative algorithm, based on ideas from the robust statistics literature and elsewhere, to address this issue and to improve robustness to attribute noise. We show experimentally the effectiveness of the approach, and the problems that arise, when adding new examples and classes.