PARADIGM, a Decision Making Framework for Variable Selection and Reduction in High Energy Physics
Sergei Gleyzer, Harrison Bertrand Prosper · 2009
In high energy physics, variable selection and reduction are key to conducting robust multivariate analyses.Initial variable selection often results in variable sets with greater cardinality than the number of degrees of freedom of the underlying model.This motivates the need for variable reduction, and more fundamentally, for a consistent decision making framework.Such a framework called PARADIGM, based on a global reduction measure called the global loss function and relevant for searches for new phenomena in physics, is described in detail.We illustrate the common pitfalls of variable selection and reduction, such as variable interactions and variable shadowing, and show that PARADIGM gives consistent results in their presence.In this paper, we discuss the application of PARADIGM to several searches for new phenomena in high energy physics and compare the performance of different measures of relative variable importance, in particular of those based on binary regression.Finally, we describe a technique called variable amplification and show how PARADIGM can be used to improve classification performance.