Scale Genetic Programming for large Data Sets: Case of Higgs Bosons Classification
Hmida, Hmida; Ben Hamida, Sana; Borgi, Amel; Rukoz, Marta (2018), Scale Genetic Programming for large Data Sets: Case of Higgs Bosons Classification, Procedia Computer Science, 126, p. 302-311. 10.1016/j.procs.2018.07.264
Type
Article accepté pour publication ou publiéDate
2018Journal name
Procedia Computer ScienceVolume
126Publisher
Elsevier
Pages
302-311
Publication identifier
Metadata
Show full item recordAuthor(s)
Hmida, HmidaLaboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Ben Hamida, Sana

Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Borgi, Amel
Laboratoire d'Informatique, Programmation, Algorithmique et Heuristique [LIPAH]
Rukoz, Marta
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
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.Subjects / Keywords
Cartesian Genetic Programming; Active Sampling; Higgs Bosons Classification; large dataset; Machine LearningRelated items
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Ben Hamida, Sana; Hmida, Hmida; Borgi, Amel; Rukoz, Marta (2019) Article accepté pour publication ou publié