Probability on Graphical Structure: A Knowledge-Based Agricultural Case

Paula Ianishi, Oilson Alberto Gonzatto Junior, Marcos Jardel Henriques, Diego Carvalho do Nascimento (), Gabriel Kamada Mattar, Pedro Luiz Ramos, Anderson Ara and Francisco Louzada
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Paula Ianishi: University of São Paulo
Oilson Alberto Gonzatto Junior: University of São Paulo
Marcos Jardel Henriques: University of São Paulo
Diego Carvalho do Nascimento: University of São Paulo
Gabriel Kamada Mattar: University of São Paulo
Pedro Luiz Ramos: University of São Paulo
Anderson Ara: Universidade Federal da Bahia
Francisco Louzada: University of São Paulo

Annals of Data Science, 2022, vol. 9, issue 2, No 9, 327-345

Abstract: Abstract This paper provides a rich framework to estimate the causal relationship among eighteen features (related to the product type and classification) on an agronomy study by using Bayesian Networks, which are a type of probabilistic graphical model. Thereby, with this class of models, we aimed to classify and identify the complaints based on corn seed commercialization. Simulation studies were used to compare both adopted algorithms, K2 and PC, and their hybrid version. These studies indicate excellent classification performance, given the knowledge of the network structure. After the estimated Directed Acyclic Graph, three features (Brand, Germination percentage, and Amount of commercialized bags) were evidenced as Impacting factors in the complaints based on corn seed commercialization.

Keywords: Bayesian networks; Classification; Causal inference (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s40745-020-00311-y

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