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Erschienen in:
Modeling Conflicts Between States: New Developments for an Old Problem Kapitel lesen Erstes Kapitel lesen

2011 | OriginalPaper | Buchkapitel

6. Fuzzy 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 investigates the level of transparency of the Takagi-Sugeno neuro-fuzzy model and the support vector machines model by applying them to conflict management, an application which is concerned with causal interpretations of results. The data set used in this investigation is the militarized interstate disputes dataset obtained from the Correlates of War (COW) project. In this chapter, a support vector machine model is trained to predict conflict. Knowledge from the Takagi-Sugeno neuro-fuzzy model is extracted by interpreting the model’s fuzzy rules and their outcomes. It is found that the Takagi-Sugeno neuro-fuzzy model offers some transparency which helps in understanding conflict management. The Takagi-Sugeno neuro-fuzzy model was compared to the support vector machine model and it was found that even though the support vector machine shows marginal advantage over the Takagi-Sugeno neuro-fuzzy model in terms of predictive capacity, the Takagi-Sugeno neuro-fuzzy model allows for linguistics interpretation.

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Vorheriges Kapitel Support Vector Machines for Modeling Interstate Conflict
Nächstes Kapitel Rough Sets for Modeling Interstate Conflict
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Metadaten
Titel
Fuzzy Sets for Modeling Interstate Conflict
verfasst von
Tshilidzi Marwala
Dr. Monica Lagazio
Copyright-Jahr
2011
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_6