Fast Feature Selection in a GPU Cluster Using the Delta Test

Entropy

Ignacio Rojas¹  M. Isabel García Arenas¹  Héctor Pomares¹  Luis Javier Herrera¹  Alberto Guillén¹  Mark van Heeswijk²  Dusan Sovilj²  Amaury Lendasse² 

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

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

DOI  :  10.3390/e16020854

来源: DOAJ

【 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】

Unknown