Design of a optimization algorithm for binary classification

• Autores: Cano Lengua, Miguel Ángel, Papa Quiroz, Erik Alex, Alvarado Cifuentes, Marco Antonio, Alvarado Cifuentes, Carlos Antonio
• Formato: artículo
• Fecha de Publicación: 2023
• Institución: Universidad Tecnológica del Perú
• Repositorio: UTP-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.utp.edu.pe:20.500.12867/7020
• Enlace del recurso: https://hdl.handle.net/20.500.12867/7020; https://doi.org/10.11591/ijeecs.v30.i3.pp1596-1608
• Nivel de acceso: acceso abierto
• Materia: Machine learning; Binary classification; Algorithm of proximal multipliers; Mathematical optimization; https://purl.org/pe-repo/ocde/ford#1.02.00

In the present work, the design of a system to classify data is carried out, using the Scrum methodology. The validation was carried out by expert judgment, having favorable results in terms of different criteria such as; integrity, ease of use, innovation, and scalability. Regarding the development of the functional elements of the system, it was obtained; he developed the architecture of the system, the database, and the prototypes, among other points considered. From the implementation of the system, the equation of a classifying plane in three-dimensional space will be obtained, as well as the number of internal iterations that the algorithm develops, the estimated execution time, and the graph of the plane. This system is based on a recently introduced symmetric cone proximal multiplier algorithm to solve separable optimization problems, this algorithm made an application for classification-related support vector machines.