Entropy 2014, 16(2), 854-869; https://doi.org/10.3390/e16020854
Fast Feature Selection in a GPU Cluster Using the Delta Test
1
Department of Computer Architecture and Computer Technology, Universidad de Granada, Granada 18071, Spain
2
Department of Information and Computer Science, Aalto University School of Science, Espoo 02150, Finland
3
IKERBASQUE, Basque Foundation for Science, Bilbao 48011, Spain
4
Arcada University of Applied Sciences, 00550 Helsinki, Finland
*
Author to whom correspondence should be addressed.
Received: 13 October 2013 / Revised: 10 January 2014 / Accepted: 28 January 2014 / Published: 13 February 2014
(This article belongs to the Special Issue Big Data)
Abstract
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. View Full-TextKeywords:
general-purpose computing on graphics processing units (GPGPU); feature selection; variable selection; big data
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).
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MDPI and ACS Style
Guillén, A.; García Arenas, M.I.; van Heeswijk, M.; Sovilj, D.; Lendasse, A.; Herrera, L.J.; Pomares, H.; Rojas, I. Fast Feature Selection in a GPU Cluster Using the Delta Test. Entropy 2014, 16, 854-869.