Algoritmos meta heurísticos para el aprendizaje de redes bayesianas
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Date
2018
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Corporación Universitaria Lasallista, Editorial Lasallista
Abstract
Introduction This article aims to obtain
models based on probabilistic case
analysis to help decision-making in
the education and learning of UTEQ
students. To obtain the final product,
the development process has been
distributed in several stages. Objective
To create a probabilistic model to
evaluate and diagnose students based
on a set of characteristics, which must
be learned automatically through a
generalization of the AutoClass model
allowing the existence of hidden
variables, each of them affecting a set
different from observable variables
(students’ answers to questions raised
by an automatic learning system).
Materials and methods. Our study will
be carried out to define another form of
structural learning based on the search
of structures through evolutionary metaheuristic models. Results This model
will allow the authorities of the UTEQ to
identify inconveniences and setbacks
in the teaching-learning process. At
the same time, the results obtained will
allow immediate decision-making to
solve the problems detected and thus
fulfill the institutional mission of training
professionals with a scientific and
humanistic vision capable of developing
research, creating technologies,
maintaining and disseminating our
ancestral knowledge and culture, for
the construction of solutions to the
problems of the region and the country.
Conclusions were metaheuristic
variable mesh optimization (VMO) to
structural learning of Bayesian network
classifiers (BVMO).
Description
Keywords
Corporación Universitaria Lasallista, Evaluación académica, Heurística, Educación - Métodos de enseñanza
Citation
Revista Lasallista de Investigación Vol. 15 No 2 2018