Determining Electoral Preferences in Mexican Voters by Computational Intelligence Algorithms

In the context of political activities, electoral processes are of interest for scientists, 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 voting 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.