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2011 | OriginalPaper | Buchkapitel

11. Neuro-Rough Sets for Modeling Interstate Conflict

verfasst von : Tshilidzi Marwala, Dr. Monica Lagazio

Erschienen in: Militarized Conflict Modeling Using Computational Intelligence

Verlag: Springer London

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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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Vorheriges Kapitel Genetic Algorithm with Optimized Rough Sets for Modeling Interstate Conflict

Nächstes Kapitel Early Warning and Conflict Prevention Using Computational Techniques

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Titel: Neuro-Rough Sets for Modeling Interstate Conflict
verfasst von: Tshilidzi Marwala
Dr. Monica Lagazio
Verlag: Springer London
Buch: Militarized Conflict Modeling Using Computational Intelligence
Print ISBN: 978-0-85729-789-1

Electronic ISBN: 978-0-85729-790-7

Copyright-Jahr: 2011
DOI: https://doi.org/10.1007/978-0-85729-790-7_11

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