Paralelização de algoritmos APS e Firefly para seleção de variáveis em problemas de calibração multivariada

• Ano de defesa: 2014
• Autor(a) principal: Paula, Lauro Cássio Martins de
• Orientador(a): Soares, Anderson da Silva
• Banca de defesa: Soares, Anderson da Silva, Martins, Wellington Santos, Coelho, Clarimar José
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Goiás
• Programa de Pós-Graduação: Programa de Pós-graduação em Ciência da Computação (INF)
• Departamento: Instituto de Informática - INF (RG)
• País: Brasil
• Palavras-chave em Português: Algoritmo das projeções sucessivas; Seleção de variáveis; Calibração multivariada; GPU; Regressão linear múltipla
• Palavras-chave em Inglês: Algoritmo firefly; Successive projections algorithm; Variable selection; Multivariate calibration; Multiple linear regression; Firefly algorithm
• Área do conhecimento CNPq: TEORIA DA COMPUTACAO::ANALISE DE ALGORITMOS E COMPLEXIDADE DE COMPUTACAO
• Link de acesso: http://repositorio.bc.ufg.br/tede/handle/tede/3418

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.