Fast feature selection in a GPU cluster using the Delta Test
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AutorGuillén Perales, Alberto; García Arenas, María Isabel; Heeswijk, Mark van; Sovilj, Dusan; Lendasse, Amaury; Herrera Maldonado, Luis Javier; Pomares Cintas, Héctor; Rojas Ruiz, Ignacio
General purpose computing on graphics processing units (GPGPU)Feature selectionVariable selectionBig data
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]
PatrocinadorThis 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.
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.