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Please use this identifier to cite or link to this item: http://hdl.handle.net/10481/31886

Title: Fast feature selection in a GPU cluster using the Delta Test
Authors: Guillén Perales, Alberto
García Arenas, María Isabel
Heeswijk, Mark van
Sovilj, Dusan
Lendasse, Amaury
Herrera Maldonado, Luis Javier
Pomares Cintas, Héctor
Rojas Ruiz, Ignacio
Issue Date: 2014
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.
Sponsorship: This work was supported in part by the Consejería de Innovación, Ciencia y Empresa of the Spanish Junta de Andalucía, under Project TIC2906 and in part by the Spanish Ministry of Science and Innovation under Project SAF2010-20558.
Publisher: MDPI
Keywords: General purpose computing on graphics processing units (GPGPU)
Feature selection
Variable selection
Big data
URI: http://hdl.handle.net/10481/31886
ISSN: 1099-4300
Rights : Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License
Citation: Guillén, A.; et al. Fast feature selection in a GPU cluster using the Delta Test. Entropy, 16: 854-869 (2014). [http://hdl.handle.net/10481/31886]
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