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

被引:6
作者
Hmida, Hmida [1 ,2 ]
Ben Hamida, Sana [2 ]
Borgi, Amel [1 ]
Rukoz, Marta [2 ]
机构
[1] Tunis El Manar Univ, LIPAH, Tunis, Tunisia
[2] PSL Res Univ, Paris Dauphine Univ, CNRS, LAMSADE,UMR 7243, F-75016 Paris, France
来源
KNOWLEDGE-BASED AND INTELLIGENT INFORMATION & ENGINEERING SYSTEMS (KES-2018) | 2018年 / 126卷
关键词
Cartesian Genetic Programming; Active Sampling; Higgs Bosons Classification; large dataset; Machine Learning;
D O I
10.1016/j.procs.2018.07.264
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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. (C) 2018 The Authors. Published by Elsevier Ltd.
引用
收藏
页码:302 / 311
页数:10
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