Aplicação de Algoritmos de Aprendizado de Máquina na Análise da Vulnerabilidade Social e Insegurança Alimentar
Resumo
A fome é um problema político e econômico com profundas repercussões sociais, sendo a vulnerabilidade social um indicador essencial para essa compreensão. Este estudo aplica métodos de aprendizado de máquina para analisar a insegurança alimentar no contexto da vulnerabilidade social. Dados socioeconômicos da Fundação SEADE, RAIS e CAISAN foram usados para gerar modelos de classificação que alcançaram F-score de 80% a 87% na categorização do Índice Paulista de Vulnerabilidade Social (IPVS). A renda domiciliar destacou-se como o fator mais relevante. Os resultados corroboram estudos anteriores, apontando que dados socioeconômicos podem ser explorados na identificação de indicadores de vulnerabilidade e insegurança alimentar.
Palavras-chave:
aprendizado de máquina, vulnerabilidade social, insegurança alimentar, dados socioeconômicos, renda domiciliar
Referências
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Sharif, Z. and Ang, M. (2001). Assessment of food insecurity among low income households in kuala lumpur using the radimer/cornell food insecurity instrument - a validation study. Malaysian journal of nutrition, 7:15–32.
Silva, M., Raposo, I., Silva, L., Assunção, J., Rolim, T., Souza, A., and Franco, F. (2020). VULNERABILIDADE SOCIAL, FOME E POBREZA NAS REGIÕES NORTE E NORDESTE DO BRASIL, pages 1083–1105.
Singh, A., Thakur, N., and Sharma, A. (2016). A review of supervised machine learning algorithms. In 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom), pages 1310–1315.
Wong, T.-T. (2015). Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation. Pattern Recognition, 48(9):2839–2846.
Yuanyuan, S., Yongming, W., Lili, G., Zhongsong, M., and Shan, J. (2017). The comparison of optimizing svm by ga and grid search. In 2017 13th IEEE International Conference on Electronic Measurement Instruments (ICEMI), pages 354–360.
Fan, Y., Xuan, L., Qifeng, Z., and Linkai, L. (2011). Margin based variable importance for random forest. In 2011 6th International Conference on Computer Science Education (ICCSE), pages 1361–1366.
Fayyad, U., Piatetsky-Shapiro, G., and Smyth, P. (1996). From data mining to knowledge discovery in databases. AI Magazine, 17(3):37.
Furness, B. W., Simon, P. A., Wold, C. M., and Asarian-Anderson, J. (2004). Prevalence and predictors of food insecurity among low-income households in los angeles county. Public Health Nutrition, 7(6):791–794.
Imron, M. A. and Prasetyo, B. (2020). Improving algorithm accuracy k-nearest neighbor using z-score normalization and particle swarm optimization to predict customer churn. Journal of Soft Computing Exploration, 1(1):56–62. Accessed: 30 Aug 2024.
Kolisetty, V. and Rajput, D. (2019). A review on the significance of machine learning for data analysis in big data. Jordanian Journal of Computers and Information Technology, 06:1.
Pérez-Escamilla, R. and Segall-Corrêa, A. M. (2008). Food insecurity measurement and indicators. Revista de Nutrição, 21:15s–26s.
Riyanto, S., Sitanggang, I., Djatna, T., and Atikah, T. (2023). Comparative analysis using various performance metrics in imbalanced data for multi-class text classification. International Journal of Advanced Computer Science and Applications, 14.
Sharif, Z. and Ang, M. (2001). Assessment of food insecurity among low income households in kuala lumpur using the radimer/cornell food insecurity instrument - a validation study. Malaysian journal of nutrition, 7:15–32.
Silva, M., Raposo, I., Silva, L., Assunção, J., Rolim, T., Souza, A., and Franco, F. (2020). VULNERABILIDADE SOCIAL, FOME E POBREZA NAS REGIÕES NORTE E NORDESTE DO BRASIL, pages 1083–1105.
Singh, A., Thakur, N., and Sharma, A. (2016). A review of supervised machine learning algorithms. In 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom), pages 1310–1315.
Wong, T.-T. (2015). Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation. Pattern Recognition, 48(9):2839–2846.
Yuanyuan, S., Yongming, W., Lili, G., Zhongsong, M., and Shan, J. (2017). The comparison of optimizing svm by ga and grid search. In 2017 13th IEEE International Conference on Electronic Measurement Instruments (ICEMI), pages 354–360.
Publicado
05/12/2024
Como Citar
S. LOBO, Pedro L.; SALVINI, Rogerio; FELIX, Juliana Paula.
Aplicação de Algoritmos de Aprendizado de Máquina na Análise da Vulnerabilidade Social e Insegurança Alimentar. In: ESCOLA REGIONAL DE INFORMÁTICA DE GOIÁS (ERI-GO), 12. , 2024, Ceres/GO.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 158-167.
DOI: https://doi.org/10.5753/erigo.2024.4792.
