Publicação
Feature Selection using LAID and its Implementation on High-Performance Computing Systems
| Resumo: | From the set of dimensionality reduction techniques, we focus on the feature selection, a possible approach to carry it out is the use of Logical Analysis of Inconsistent Data (LAID). Recently, studies have demonstrated its potential in solving this problem and highlighted its advantages as a robust methodology, easy to interpret, and additionally capable of dealing with inconsistent data. The same studies revealed processing times higher than desired for full use and recommended the execution of algorithms through parallel processing using high-performance computing (HPC). This work represents yet another contribution to this effort by addressing ways of storing data, solutions for parallelizing algorithms, configuring the HPC environment, and finally testing the National Infrastructure for Distributed Computing (INCD) that allowed us to draw the conclusions presented. |
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| Autores principais: | Morgado, Paulo |
| Outros Autores: | Cavique, Luís |
| Assunto: | Ciência de dados Seleção de atributos Análise lógica de dados inconsistentes (LAID) Computação paralela Data science Features selection Logical Analysis of Inconsistent Data (LAID) Parallel computing |
| Ano: | 2021 |
| País: | Portugal |
| Tipo de documento: | artigo |
| Tipo de acesso: | unknown |
| Instituição associada: | Universidade Aberta |
| Idioma: | português |
| Origem: | Revista de Ciências da Computação |
| Resumo: | From the set of dimensionality reduction techniques, we focus on the feature selection, a possible approach to carry it out is the use of Logical Analysis of Inconsistent Data (LAID). Recently, studies have demonstrated its potential in solving this problem and highlighted its advantages as a robust methodology, easy to interpret, and additionally capable of dealing with inconsistent data. The same studies revealed processing times higher than desired for full use and recommended the execution of algorithms through parallel processing using high-performance computing (HPC). This work represents yet another contribution to this effort by addressing ways of storing data, solutions for parallelizing algorithms, configuring the HPC environment, and finally testing the National Infrastructure for Distributed Computing (INCD) that allowed us to draw the conclusions presented. |
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