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

2011 | OriginalPaper | Buchkapitel

7. 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 applies the rough set technique to model militarized interstate disputes. One aspect of modeling using rough sets is the issue of granulizing the input data. In this chapter, two granulization techniques are introduced, implemented, and compared. These are the equal-width-bin and equal-frequency-bin partitioning techniques. The rough set model is also compared to the neuro-fuzzy model introduced in Chap.​ 6. The results obtained demonstrate that equal-width-bin partitioning gives better accuracy than equal-frequency-bin partitioning. However, both techniques were found to give less accurate results than neuro-fuzzy sets. Also, they were found to be more transparent than neuro-rough sets. Furthermore, it is observed that the rules generated from the rough sets are linguistic and easy-to-interpret in comparison with the ones generated from the neuro-fuzzy model.

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Vorheriges Kapitel Fuzzy Sets for Modeling Interstate Conflict
Nächstes Kapitel Particle Swarm Optimization and Hill-Climbing Optimized Rough Sets for Modeling Interstate Conflict
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Metadaten
Titel
Rough 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_7