2006/05/31 by J. M. Conrad, J. Conrad, F. Tegenfeldt
Mathematics · Physics and Astronomy · #Algorithm #Artificial intelligence #Artificial neural network #Combinatorics #Computer science #Decision tree #Ensemble learning #High-Energy Particle Collisions Research #Large Hadron Collider #Machine learning #Mathematics #Parameter space #Particle physics #Particle physics theoretical and experimental studies #Physics #Quantum Chromodynamics and Particle Interactions #Random forest #Set (abstract data type) #Statistics #Sum rule in quantum mechanics #Tree (set theory) #hep-ph
paper · pdf · doi:10.1088/1126-6708/2006/07/040
published as JHEP 0607:040,2006 · 24 pages, 7 figures, replaced to match version accepted for publication in JHEP
arxiv created 2006/06/29 · openalex publication_date 2006/07/24 · arxiv updated 2011/01/13 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/05
In this note we give an example application of a recently presented predictive learning method called Rule Ensembles. The application we present is the search for super-symmetric particles at the Large Hadron Collider. In particular, we consider the problem of separating the background coming from top quark production from the signal of super-symmetric particles. The method is based on an expansion of base learners, each learner being a rule, i.e. a combination of cuts in the variable space describing signal and background. These rules are generated from an ensemble of decision trees. One of the results of the method is a set of rules (cuts) ordered according to their importance, which gives useful tools for diagnosis of the model. We also compare the method to a number of other multivariate methods, in particular Artificial Neural Networks, the likelihood method and the recently presented boosted decision tree method. We find better performance of Rule Ensembles in all cases. For example for a given significance the amount of data needed to claim SUSY discovery could be reduced by 15 % using Rule Ensembles as compared to using a likelihood method.