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Scale Genetic Programming for large Data Sets: Case of Higgs Bosons Classification

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Date
2018
Dewey
Intelligence artificielle
Sujet
Cartesian Genetic Programming; Active Sampling; Higgs Bosons Classification; large dataset; Machine Learning
Journal issue
Procedia Computer Science
Volume
126
Publication date
08-2018
Article pages
302-311
Publisher
Elsevier
DOI
http://dx.doi.org/10.1016/j.procs.2018.07.264
URI
https://basepub.dauphine.fr/handle/123456789/18537
Collections
  • LAMSADE : Publications
Metadata
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Author
Hmida, Hmida
989 Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Ben Hamida, Sana
989 Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Borgi, Amel
253759 Laboratoire d'Informatique, Programmation, Algorithmique et Heuristique [LIPAH]
Rukoz, Marta
989 Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Type
Article accepté pour publication ou publié
Abstract (EN)
Extract knowledge and significant information from very large data sets is a main topic in Data Science, bringing the interest of researchers in machine learning field. Several machine learning techniques have proven effective to deal with massive data like Deep Neuronal Networks. Evolutionary algorithms are considered not well suitable for such problems because of their relatively high computational cost. This work is an attempt to prove that, with some extensions, evolutionary algorithms could be an interesting solution to learn from very large data sets. We propose the use of the Cartesian Genetic Programming (CGP) as meta-heuristic approach to learn from the Higgs big data set. CGP is extended with an active sampling technique in order to help the algorithm to deal with the mass of the provided data. The proposed method is able to take up the challenge of dealing with the complete benchmark data set of 11 million events and produces satisfactory preliminary results.

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