Multivariate decision tree design for the classification of multi-jet topologies in $e^{+}e^{-}$ collisions

The binary decision tree method is used to separate between several multi-jet topologies in e/sup +/e/sup -/ collisions. Instead of the univariate process usually taken, a new design procedure for constructing multivariate decision trees is proposed. The segmentation is obtained by considering some...

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Detalles Bibliográficos
Autor principal: Mjahed, M
Lenguaje:eng
Publicado: 2002
Materias:
Detectors and Experimental Techniques
Acceso en línea:https://dx.doi.org/10.1016/S0168-9002(01)01339-0
http://cds.cern.ch/record/588648
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author Mjahed, M
author_facet Mjahed, M
author_sort Mjahed, M
collection CERN
description The binary decision tree method is used to separate between several multi-jet topologies in e/sup +/e/sup -/ collisions. Instead of the univariate process usually taken, a new design procedure for constructing multivariate decision trees is proposed. The segmentation is obtained by considering some features functions, where linear and nonlinear discriminant functions and a minimal distance method are used. The classification focuses on ALEPH simulated events, with multi-jet topologies. Compared to a standard univariate tree, the multivariate decision trees offer significantly better performance. (30 refs).
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institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2002
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spelling cern-5886482019-09-30T06:29:59Zdoi:10.1016/S0168-9002(01)01339-0http://cds.cern.ch/record/588648engMjahed, MMultivariate decision tree design for the classification of multi-jet topologies in $e^{+}e^{-}$ collisionsDetectors and Experimental TechniquesThe binary decision tree method is used to separate between several multi-jet topologies in e/sup +/e/sup -/ collisions. Instead of the univariate process usually taken, a new design procedure for constructing multivariate decision trees is proposed. The segmentation is obtained by considering some features functions, where linear and nonlinear discriminant functions and a minimal distance method are used. The classification focuses on ALEPH simulated events, with multi-jet topologies. Compared to a standard univariate tree, the multivariate decision trees offer significantly better performance. (30 refs).oai:cds.cern.ch:5886482002
spellingShingle Detectors and Experimental Techniques
Mjahed, M
Multivariate decision tree design for the classification of multi-jet topologies in $e^{+}e^{-}$ collisions
title Multivariate decision tree design for the classification of multi-jet topologies in $e^{+}e^{-}$ collisions
title_full Multivariate decision tree design for the classification of multi-jet topologies in $e^{+}e^{-}$ collisions
title_fullStr Multivariate decision tree design for the classification of multi-jet topologies in $e^{+}e^{-}$ collisions
title_full_unstemmed Multivariate decision tree design for the classification of multi-jet topologies in $e^{+}e^{-}$ collisions
title_short Multivariate decision tree design for the classification of multi-jet topologies in $e^{+}e^{-}$ collisions
title_sort multivariate decision tree design for the classification of multi-jet topologies in $e^{+}e^{-}$ collisions
topic Detectors and Experimental Techniques
url https://dx.doi.org/10.1016/S0168-9002(01)01339-0
http://cds.cern.ch/record/588648
work_keys_str_mv AT mjahedm multivariatedecisiontreedesignfortheclassificationofmultijettopologiesineecollisions

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