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Fast Feature Selection in a GPU Cluster Using the Delta Test

Alberto Guillen, M. Isabel Garcia Arenas, Mark van Heeswijk, Dusan Sovilj, Amaury Lendasse, Luis Javier Herrera, Hector Pomares, Ignacio Rojas
Department of Computer Architecture and Computer Technology, Universidad de Granada, Granada 18071, Spain
Entropy, 16(2), 854-869, 2014

@Article{e16020854,

   author={Guillen, Alberto and Garcia Arenas, M. Isabel and van Heeswijk, Mark and Sovilj, Dusan and Lendasse, Amaury and Herrera, Luis Javier and Pomares, Hector and Rojas, Ignacio},

   title={Fast Feature Selection in a GPU Cluster Using the Delta Test},

   journal={Entropy},

   volume={16},

   year={2014},

   number={2},

   pages={854–869},

   url={http://www.mdpi.com/1099-4300/16/2/854},

   issn={1099-4300},

   doi={10.3390/e16020854}

}

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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.
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