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.
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The authors are grateful for the funding partially provided by the Brazilan agencies CNPq, FAPESP and CAPES. Francisco Louzada and Pedro L. Ramos acknowledge support from the São Paulo State Research Foundation (FAPESP Processes 2013/07375-0 and 2017/25971-0, respectively). The authors are also thankful for the collaboration of Camila Sgarioni Ozelame, Herlisson Maciel Bezerra, Cláudio Luiz, Selegatto Filho, Leonardo Alves Miguel, Lucas Santos Nunez, Mariane Romildo dos Santos, Matheus de Oliveira Souza, and Paulo Lombardi.
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Ianishi, P., Gonzatto Junior, O.A., Henriques, M.J. et al. Probability on Graphical Structure: A Knowledge-Based Agricultural Case. Ann. Data. Sci. 9, 327–345 (2022). https://doi.org/10.1007/s40745-020-00311-y