Paralelização de algoritmos APS e Firefly para seleção de variáveis em problemas de calibração multivariada
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Data
2014-07-15
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Universidade Federal de Goiás
Resumo
The problem of variable selection is the selection of attributes for a given sample that
best contribute to the prediction of the property of interest. Traditional algorithms as
Successive Projections Algorithm (APS) have been quite used for variable selection in
multivariate calibration problems. Among the bio-inspired algorithms, we note that the
Firefly Algorithm (AF) is a newly proposed method with potential application in several
real world problems such as variable selection problem. The main drawback of these tasks
lies in them computation burden, as they grow with the number of variables available.
The recent improvements of Graphics Processing Units (GPU) provides to the algorithms
a powerful processing platform. Thus, the use of GPUs often becomes necessary to
reduce the computation time of the algorithms. In this context, this work proposes a
GPU-based AF (AF-RLM) for variable selection using multiple linear regression models
(RLM). Furthermore, we present two APS implementations, one using RLM (APSRLM)
and the other sequential regressions (APS-RS). Such implementations are aimed at
improving the computational efficiency of the algorithms. The advantages of the parallel
implementations are demonstrated in an example involving a large number of variables.
In such example, gains of speedup were obtained. Additionally we perform a comparison
of AF-RLM with APS-RLM and APS-RS. Based on the results obtained we show that the
AF-RLM may be a relevant contribution for the variable selection problem.
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Citação
PAULA, Lauro Cássio Martins de. Paralelização de algoritmos APS e Firefly para seleção de variáveis em problemas de calibração multivariada. 2014. 81 f. Dissertação (Mestrado em Ciência da Computação) - Universidade Federal de Goiás, Goiânia, 2014.