Reducing Economic Costs as an Explicit Requirement for Machine-Learning-Based Classifiers

With fusion research pushing toward commercialization, there's no time like the present to start thinking about how economic costs play into various aspects of our research. Along those lines, machine learning tools offer cost savings in a variety of ways, including by reducing computing resources needed for complex simulations and real-time analysis, by guiding the use of limited experimental resources through improved predictive modeling, and even by reducing damage to tokamak reactor components through improved disruption prediction. In this work, we will focus on discussing how cost reduction can be used as an explicit requirement for machine-learning-based classifiers. In this intuitive approach we will look at how the balance between the True Positive and False Positive Rates of classification represents a tradeoff of real economic costs, and how a minimum performance threshold can be derived from these costs. The cost-reduction threshold provides both a necessary and sufficient condition for implementing any classifier, and can further be used to assess which classifier provides the best cost savings. We will look at recent examples from the literature on disruption prediction as an illustration of how to use this cost-based framework for assessing classifiers.