An electoral preferences model based on self-organizing maps

Neme, Antonio; Hernández, S.; Castillo, Omar Neme

doi:10.1016/j.jocs.2011.08.003

Cited by 6 publications

(4 citation statements)

References 27 publications

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“…Neural networks application fields are typically those where elassie algorithms fail because of their inflexibility (they need precise input datasets) [12]. Usually problems with imprecise input datasets are those whose number of possible inputs datasets is so big that they cannot be classified.…”

Section: Neural Networkmentioning

confidence: 99%

The optimal combination: Grammatical swarm, particle swarm optimization and neural networks

López

Blas

Arteta

2012

Journal of Computational Science

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Social behaviour is mainly based on swarm colonies, in which each individual shares its knowledge about the environment with other individuáis to get optimal solutions. Such co-operative model differs from competitive models in the way that individuáis die and are born by combining information of alive ones. This paper presents the particle swarm optimization with differential evolution algorithm in order to train a neural network instead the classic back propagation algorithm. The performance of a neural network for particular problems is critically dependant on the choice of the processing elements, the net architecture and the learning algorithm. This work is focused in the development of methods for the evolutionary design of artificial neural networks. This paper focuses in optimizing the topology and structure of connectivity for these networks.

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Section: Neural Networkmentioning

confidence: 99%

The optimal combination: Grammatical swarm, particle swarm optimization and neural networks

López

Blas

Arteta

2012

Journal of Computational Science

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“…Studies have used various clustering techniques to quantify the different levels of agreement among climate change opinions, including hierarchical cluster analysis (Barnes and Toma 2012;Jones and Song 2014), classification tree models (Lee et al 2015), and audience segmentation analysis (Maibach et al 2011;Rolfe-Redding et al 2011;Detenber et al 2016). A few of them conducted general opinion and sentiment analysis research using self-organizing maps (SOM) as a spatiotemporal clustering method (Sharma and Dey 2013;Neme et al 2011;Janetzko et al 2013), though none of these explicitly focused on climate change opinions. There remains some concern that these and similar methods may contribute to increased local polarization, do little to promote changing values on climate change, and can reflect a particular researcher's methodological preference (Hine et al 2014).…”

Section: Introductionmentioning

confidence: 99%

Exploratory Geovisualization of the Character and Distribution of American Climate Change Beliefs

Sugg

2021

Weather, Climate, and Society

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Americans remain polarized about climate change. However, recent scholarship reveals a plurality of climate change opinions among the public, with non-trivial support for a range of awareness, risk perceptions, and policy prescriptions. This study uses publicly available opinion estimates to examine the geographic variability of American climate change opinions and maps them as regions, which share similarities or differences in the character of their beliefs. The exploratory geovisual environment of a self-organizing map is used to compare the support for 56 different climate opinions across all counties in the continental US and arrange them into a spatially coherent grid of nodes. To facilitate the exploration of the patterns, a statistical cluster analysis groups together counties with the most similar climate beliefs. Choropleth maps visualize the clustering results from the self-organizing map. This study finds six groups of climate beliefs, in which member counties exhibit a distinct regionality across the US, and share similarities in the magnitude of support for specific opinions. Groups that generally exhibit high or low levels of support for climate change awareness, risk perceptions, and policy prescriptions vary in their relative support for specific opinions. The results provide a nuanced understanding of different types of climate change opinions and where they exist geographically.

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“…Tuia, et al, have used a SOM for the clustering of urban municipalities in Switzerland depending on their socio-economic profile [22]. The mechanism for self-organization in a SOM has even been used in a non-computational way as a metaphor to explain the patterns in electoral processes [16,17]. The second author has used SOMs to identify benchmark universities on the basis of student assessment of university websites [4].…”

Section: Introductionmentioning

confidence: 99%

Using Self Organizing Maps to Analyze Demographics and Swing State Voting in the 2008 U.S. Presidential Election

Pearson

Cooper

2012

Artificial Neural Networks in Pattern Recognition

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Emergent self-organizing maps (ESOMs) and k-means clustering are used to cluster counties in each of the states of Florida, Pennsylvania, and Ohio by demographic data from the 2010 United States census. The counties in these clusters are then analyzed for how they voted in the 2008 U.S. Presidential election, and political strategies are discussed that target demographically similar geographical regions based on ESOM results. The ESOM and k-means clusterings are compared and found to be dissimilar by the variation of information distance function.

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An electoral preferences model based on self-organizing maps

Cited by 6 publications

References 27 publications

The optimal combination: Grammatical swarm, particle swarm optimization and neural networks

The optimal combination: Grammatical swarm, particle swarm optimization and neural networks

Exploratory Geovisualization of the Character and Distribution of American Climate Change Beliefs

Using Self Organizing Maps to Analyze Demographics and Swing State Voting in the 2008 U.S. Presidential Election

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