Open AccessThe machine learning in the prediction of elections
Author(s) -
José A. León-Borges,
Roger-Ismael Noh-Balam,
Lino Rangel Gómez,
Michael Philip Strand
Publication year - 2015
Publication title -
recibe
Language(s) - English
Resource type - Journals
ISSN - 2007-5448
DOI - 10.32870/recibe.v4i2.36
Subject(s) - computer science , artificial intelligence , convergence (economics) , machine learning , software , data mining , state (computer science) , algorithm , economics , programming language , economic growth
This research article, presents an analysis and a comparison of three different algorithms: A.- Grouping method K-means, B.-Expectation a convergence criteria, EM and C.- Methodology for classification LAMDA, using two software of classification Weka and SALSA, as an aid for the prediction of future elections in the state of Quintana Roo. When working with electoral data, these are classified in a qualitative and quantitative way, by such virtue at the end of this article you will have the elements necessary to decide, which software, has better performance for such learning of classification.The main reason for the development of this work, is to demonstrate the efficiency of algorithms, with different data types. At the end, it may be decided, the algorithm with the better performance in data management.