Título: Classificação e Interpretação de dados do Cadastro Ambiental Rural utilizando técnicas de Aprendizagem de Máquina
Autores: Fernando Elias Melo Borges, Danton Ferreira and Antônio Couto Junior
Resumo:
The Rural Environmental Registry (CAR) consists of a mandatory public electronic registry for all rural properties in the Brazilian territory, integrates environmental information of the properties, assists the monitoring of them and the fight against deforestation. However, a large number of registrations are carried out erroneously generating inconsistent data, leading these to be canceled and/or to be requested to correct the registration. Performing automatic verification of these records is important to improve the processing of records. This paper proposes an automatic classification method to approve or cancel the CAR registers with interpretation of the classifications performed. For this, four machine learning-based classifiers were tested and the results were evaluated. The model with the best performance was used to interpret the classification using the Local Interpretable Model-agnostic Explanations (LIME) algorithm. The results showed the potential of the method in future real applications.
Palavras-chave:
Rural Environmental Registry, Data Mining, Unbalanced Data, Interpretable Machine Learning.
Páginas: 7
Código DOI: 10.21528/CBIC2021-108
Artigo em pdf: CBIC_2021_paper_108.pdf
Arquivo BibTeX: CBIC_2021_108.bib