Classificação das percepções de stakeholders sobre o futuro do Brasil utilizando aprendizado de máquina
Resumo
Este artigo compara cinco técnicas de aprendizado de máquina (AM) para classificar as percepções dos stakeholders quanto ao futuro do Brasil. As técnicas de ML utilizadas foram redes neurais artificiais, k-vizinhos mais próximos, naïve bayes, floresta aleatória e máquinas de vetores de suporte. Eles foram aplicados a um conjunto de dados do Banco Mundial sobre o desenvolvimento do Brasil. O conjunto de dados foi pré-processado e configurado em duas versões diferentes: a primeira continha um subconjunto de atributos selecionados manualmente pelos autores, enquanto a segunda era composta por atributos selecionados usando a abordagem de ganho de informação. Verificou-se que todas as técnicas de ML tiveram melhor desempenho com a segunda versão do conjunto de dados, em que os atributos foram classificados com base no ganho de informação. No entanto, dentro de cada versão do conjunto de dados, todas as técnicas tiveram desempenhos semelhantes. Esta pesquisa também constatou que os atributos mais relevantes estão relacionados às oportunidades de negócios, índices de desenvolvimento associados a temas críticos e confiança nas instituições e organizações.
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DOI: http://dx.doi.org/10.5380/atoz.v12i0.84075
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