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Militarized Conflict Modeling Using Computational Intelligence pp 201–216Cite as

Neuro-Rough Sets for Modeling Interstate Conflict

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Part of the book series: Advanced Information and Knowledge Processing ((AI&KP))

Abstract

This chapter investigated a neuro-rough model –a combination of a Multi-Layered Perceptron (MLP) neural network with rough set theory– for the modeling of interstate conflict. The model was formulated using a Bayesian framework and trained using a Monte Carlo technique with the Metropolis criterion. The model was then tested on militarized interstate dispute and was found to combine the accuracy of the Bayesian MLP model with the transparency of the rough set model. The technique presented was compared to the genetic algorithm optimized rough sets. The presented Bayesian neuro-rough model performed better than the genetic algorithm optimized rough set model.

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Authors and Affiliations

  1. University of Johannesburg, Johannesburg, South Africa

    Tshilidzi Marwala & Dr. Monica Lagazio

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  1. Tshilidzi Marwala
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  2. Dr. Monica Lagazio
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Correspondence to Tshilidzi Marwala .

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Marwala, T., Lagazio, M. (2011). Neuro-Rough Sets for Modeling Interstate Conflict. In: Militarized Conflict Modeling Using Computational Intelligence. Advanced Information and Knowledge Processing. Springer, London. https://doi.org/10.1007/978-0-85729-790-7_11

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  • DOI: https://doi.org/10.1007/978-0-85729-790-7_11

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  • Publisher Name: Springer, London

  • Print ISBN: 978-0-85729-789-1

  • Online ISBN: 978-0-85729-790-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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