Fuzzy sets for modeling interstate conflict

Tshilidzi Marwala, Monica Lagazio

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

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.

Original languageEnglish
Title of host publicationAdvanced Information and Knowledge Processing
PublisherSpringer London
Pages107-125
Number of pages19
Edition9780857297891
DOIs
Publication statusPublished - 2011

Publication series

NameAdvanced Information and Knowledge Processing
Number9780857297891
ISSN (Print)1610-3947
ISSN (Electronic)2197-8441

ASJC Scopus subject areas

  • Management Information Systems
  • Information Systems
  • Information Systems and Management
  • Artificial Intelligence

Fingerprint

Dive into the research topics of 'Fuzzy sets for modeling interstate conflict'. Together they form a unique fingerprint.

Cite this