Design of a optimization algorithm for binary classification

Abstract

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.

An improve unsupervised discretization using optimization algorithms for classification problems

“…Two classification methods are used: Naïve Bayes and KNNs. The classifier as a benchmark classifier and usually uses in classifying [25]. These classifiers used to classify the datasets DS1, DS2, DS3 and DS4 before and after discretization.…”