By Topic

Predicting marital dissolutions using radial basis function neural networks

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

7 Author(s)
A. Guillén ; Department of Computer Architecture and Technology, University of Granada. Spain ; C. Tovar ; L. J. Herrera ; H. Pomares
more authors

Marital dissolutions, that include divorces and separations, are considered one of the most adverse events that can influence the health, life quality and welfare of the adults and infants implied. There have been several researches in the sanitary and sociological field that show how these processes of breaking a life in common can deteriorate the health in both, the physic and the psychological, aspect. This paper presents the application of machine learning methods to be able to predict if a marriage, that has started a dissolution process, will end up in a friendly agreement or it will be taken to the court. In order to accomplish this task, Radial Basis Function Neural Networks in combination with Mutual Information will be able to determine which elements should be considered to make a prediction. As the experiments will show, the methodology applied is able to classify with a high accuracy and robustness a real data base. The results could be applied in order to prevent some traumas to the people involved in the dissolution in the medical aspect and to perform a better management in the legal aspects.

Published in:

The 2010 International Joint Conference on Neural Networks (IJCNN)

Date of Conference:

18-23 July 2010