One-class classification - From theory to practice: A case-study in radioactive threat detection

Over the years, the acceptance of machine learning as a valuable tool in the real-world has caused much interest in the research community; this is particularly the case as the field of Big Data is coming into prominence. However, real-world data comes with a myriad of challenges, amongst the most prominent of which is the fact that it can exhibit a high level of imbalance. This can come in the form of both within- and between-class imbalance. While a significant amount of research has been devoted to the impact of within-class imbalance over binary classifiers, very little attention has been given to their impact on one-class classifiers, which are typically used in situations of extreme between-class imbalance. During our collaboration with Health Canada into the identification of anomalous gamma-ray spectra, the issue of within-class imbalance in a one-class classification setting was highly significant. In this setting, the imbalance comes from the fact that the background data that we wish to model is composed of two concepts (background no-rain and rain); the rain sub-concept is rare and corresponds to spectra affected by the presence of water in the environment. In this article, we present our work into developing systems for detecting anomalous gamma-rays that are able to handle both the inherent between-class and within-class imbalance present in the domain. We test and validate our system over data provided to us by Health Canada from three sites across Canada. Our results indicated that oversampling the sub-concept improves the performance of the baseline classifiers and multiple classifier system when measured by the geometric mean of the per-class accuracy.