Support vector machines for predicting the level of integration in agri-food chains

Verduga-Alcívar D.A.; Muñoz-Grillo E.G.; Orozco-Crespo E.; López-Joy T.; Acevedo-Urquiaga A.J.; Sablón-Cossío N. · Ingenieria y Universidad (2023) · DOI: 10.11144/javeriana.iued27.svmp

Resumen

Objective: The objective of this paper is derived from a theoretical analysis of the application of support vector machines to the design and management of agri-food chains. This analysis is conducted using an empirical approach to predict of the level of integration in agri-food chains by support vector machines. Materials and Methods: The methodology designed and utilized to process the research results, which consists of the training of support vector machines, is employed to predict the level of integration in an agri-food chain. This type of predictive application appears in the reviewed literature on the integration of agri-food chains. Results and Discussion: The analysis is performed by comparing the proposed method with a neural network technique. The research results focus on predicting the level of integration in agri-food chains using vector machines. Conclusions: The study provides a support vector machine model that is applied to other case studies and therefore allows for the prediction of outcomes. The paper also compares two techniques that share the goal of prediction, as applied in different contexts