Fast Feature Selection in a GPU Cluster Using the Delta Test

Entropy

Alberto Guillén²  M. Isabel Garc໚ Arenas²  Mark van Heeswijk¹  Dusan Sovilj¹  Amaury Lendasse¹  Luis Javier Herrera²  Hຜtor Pomares² 

[1] Department of Information and Computer Science, Aalto University School of Science, Espoo 02150, Finland; E-Mails:;Department of Computer Architecture and Computer Technology, Universidad de Granada, Granada 18071, Spain; E-Mails:

关键词: general-purpose computing on graphics processing units (GPGPU);    feature selection;    variable selection;    big data;   

DOI  :  10.3390/e16020854

来源: mdpi

【 摘 要 】

Feature or variable selection still remains an unsolved problem, due to the infeasible evaluation of all the solution space. Several algorithms based on heuristics have been proposed so far with successful results. However, these algorithms were not designed for considering very large datasets, making their execution impossible, due to the memory and time limitations. This paper presents an implementation of a genetic algorithm that has been parallelized using the classical island approach, but also considering graphic processing units to speed up the computation of the fitness function. Special attention has been paid to the population evaluation, as well as to the migration operator in the parallel genetic algorithm (GA), which is not usually considered too significant; although, as the experiments will show, it is crucial in order to obtain robust results.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland