Determining Electoral Preferences in Mexican Voters by Computational Intelligence Algorithms

Abstract

In the context of political activities, electoral
processes are of interest for scientist, who usually tackle their
research on this field from a social sciences perspective.
Computational methods have been applied to predict the
electoral preferences of voters in several countries; however, this
has not happened in Mexico, at least as indicated by the absence
in current scientific literature of computational studies to
determine voters intentions of Mexican citizens. The authors of
the present work aim at reverting such absence. The proposal of
this paper consists of applying Computational Intelligence
methods to automatically determine electoral preferences of
Mexican voters. For this, data acquired by the Secretaría de
Gobernación (Secretary of the Interior), about voting intentions
of Mexican citizens in the 2012 elections are used. In the voter
classification stage, a modified version of the Gamma Associative
Classifier (MGAC) is used, given that this is one of the relevant
models of the Associative approach to Pattern Classification.
Additionally, Differential Evolution is employed to guide the
process of relevant features selection. Results indicate that, when
compared over six data sets extracted from the information
published by the Secretaría de Gobernación, our proposal
exhibits the best performance in three of these data sets,
outperforming some of the best similar models present in the
state of the art.