Aprendizaje estructural de redes bayesianas utilizando la meta-heurística optimización basada en mallas variables(Vmo)

In this work the study of meta-heuristic optimization Meshes Based Variables (VMO) for structural training Bayesian networks is presented. The VMO algorithm proposes two main operators, expansion and contraction of mesh solutions, which is responsible for conducting the exploration of the search space. These operators were redesigned to fit the training of Bayesian networks. Expansion operator two forms new generation networks based operations between sets and for the case of construction selection studied two forms were studied; elitist and representativeness. The results obtained after adjustment parameters VMO compared with several of Bayesian classifiers commonly used in the literature, which significantly superior results were obtained for quality classification. The VMO algorithm was incorporated into the, which was selected because of its great capacity for handling Bayesian networks and especially for being open source software Elvira. We used some of the main classes and new classes that operate according to different functional needs of the algorithm are created.

• Автор: Moreira Zamora, Luis Enriqu (author)
• Формат: masterThesis
• Мова: spa
• Опубліковано: 2015
• Предмети: Redes ayesianas; Meta-heurística; Mallas variables (Vmo)
• Онлайн доступ: http://repositorio.uteq.edu.ec/handle/43000/128