Add to ORKGAn algorithm for optimization of signal significance or any other classification figure of merit (FOM) suited for analysis of HEP data is described. This algorithm trains decision trees on many bootstrap replicas of training data with each tree required to optimize the signal significance or any other chosen FOM. New data are then classified by a simple majority vote of the built trees. The performance of the algorithm has been studied using a search for the radiative leptonic decay B → γlν at BABAR and shown to be superior to that of all other attempted classifiers including such powerful methods as boosted decision trees. In the B → γeν channel, the described algorithm increases the expected signal significance from 2.4σ obtained by an or...
Optimization is required for producing the best results. Heuristic algorithm is one of the technique...
International audienceDecision trees are a machine learning technique more and more commonly used in...
This document introduces basics in data preparation, feature selection and learning basics for high ...
This paper evaluates the performance of boosted decision trees for tagging b-jets. It is shown, usin...
Boosted decision trees are a very powerful machine learning technique. After introducing specific co...
The use of multivariate classifiers, especially neural networks and decision trees, has become commo...
The aim of this project was to follow the ATLAS $H \rightarrow WW$ BDT analysis and try to optimize ...
Machine-learning techniques have become fundamental in high-energy physics and, for new physics sear...
HEP event selection is traditionally considered a binary classification problem, involving the dicho...
HEP event selection is traditionally considered a binary classification problem, involving the dicho...
The popularity of Machine Learning (ML) has been increasing in recent decades in almost every area, ...
With the accumulation of large collision datasets at a center-of-mass energy of 13 TeV, the LHC expe...
Modern analysis of HEP data needs advanced statistical tools to separate signal from background. Thi...
I discuss the choice of evaluation metrics for binary classifiers in High Energy Physics (HEP) event...
International audienceThe Higgs boson discovery at the Large Hadron Collider in 2012 relied on boost...
Optimization is required for producing the best results. Heuristic algorithm is one of the technique...
International audienceDecision trees are a machine learning technique more and more commonly used in...
This document introduces basics in data preparation, feature selection and learning basics for high ...
This paper evaluates the performance of boosted decision trees for tagging b-jets. It is shown, usin...
Boosted decision trees are a very powerful machine learning technique. After introducing specific co...
The use of multivariate classifiers, especially neural networks and decision trees, has become commo...
The aim of this project was to follow the ATLAS $H \rightarrow WW$ BDT analysis and try to optimize ...
Machine-learning techniques have become fundamental in high-energy physics and, for new physics sear...
HEP event selection is traditionally considered a binary classification problem, involving the dicho...
HEP event selection is traditionally considered a binary classification problem, involving the dicho...
The popularity of Machine Learning (ML) has been increasing in recent decades in almost every area, ...
With the accumulation of large collision datasets at a center-of-mass energy of 13 TeV, the LHC expe...
Modern analysis of HEP data needs advanced statistical tools to separate signal from background. Thi...
I discuss the choice of evaluation metrics for binary classifiers in High Energy Physics (HEP) event...
International audienceThe Higgs boson discovery at the Large Hadron Collider in 2012 relied on boost...
Optimization is required for producing the best results. Heuristic algorithm is one of the technique...
International audienceDecision trees are a machine learning technique more and more commonly used in...
This document introduces basics in data preparation, feature selection and learning basics for high ...