Tineo Flores, DanielMurillo, Yuriko S.Marin, MercedesGomez Fernandez, DarwinTaboada, Víctor H.Goñas Goñas, MalluriQuiñonez Huatangari, Lenin2025-04-012025-04-012024-09-24Daniel Tineo et al., 'A Comparison of Classification Algorithms for Predicting Distinctive Characteristics in Fine Aroma Cocoa Flowers Using WEKA Modeler,' Qubahan Academic Journal, Vol. 4, No. 3, pp. 713-724, September 2024. DOI: 10.48161/qaj.v4n3a571http://hdl.handle.net/20.500.12955/2703Author contribution: All authors made an equal contribution to the development and planning of the study. Conflict of Interest: The authors have no potential conflicts of interest, or such divergences linked with this research study. Data Availability Statement: Data are available from the authors upon request.The expression of crop functional traits is influenced by environmental and management conditions, which in turn is reflected in genetic diversity. This study employed a data mining approach to determine the functional traits of flowers that influence cocoa diversity. A total of 1,140 flowers from 228 trees were utilized in this study, with 177 representing fine aroma cocoa trees and 51 trees belonging to other commercial cultivars. Three attribute evaluators (InfoGainAttributeEval, CorrelationAttributeEval and GainRatioAttributeEval), and six algorithms (Naive Bayes, Multinomial Logistic Regression, J48, Random Forest, LTM and Simple Logistic) were employed in this study. The findings indicated that the GainRatioAttributeEval attribute generator was the most efficacious in discerning the functional trait in cocoa diversity flowers. The algorithms Simple Logistic and LMT were the most accurate and specific, while Naive Bayes was the most efficient in terms of computational complexity for model building. This research provides a comprehensive overview of the use of machine learning to analyze functional traits of flowers that most influence cocoa genetic diversity. It also highlights the need to further improve these models by integrating additional techniques to increase their efficiency and extend the data mining approach to other agricultural sectors.Contenidos del Trabajo Depositado: Introducción Revisión de la Literatura Materiales y Métodos Área de Estudio y Descripción del Sitio Adquisición del Conjunto de Datos Proceso de Minería de Datos Selección de Atributos Técnicas de Clasificación Verificación del Rendimiento del Clasificador Resultados Selección de Características Verificación del Rendimiento del Clasificador Usando WEKA Explorer Verificación del Rendimiento del Clasificador Usando WEKA Experimenter Discusión Conclusión Declaración de Financiamiento Contribución de los Autores Conflicto de Intereses Declaración de Disponibilidad de Datos Agradecimientosapplication/pdfenginfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/4.0/Algorithmsdata miningfunctional traitsmachine learningTheobroma cacao.info:eu-repo/semantics/articlehttps://purl.org/pe-repo/ocde/ford#4.01.0110.48161/qaj.v4n3a571Cacao