Conflict modelling and knowledge extraction using computational intelligence methods

Thando Tettey, T. Marwala

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

11 Citations (Scopus)

Abstract

This paper investigates the level of transparency of the Takagi-Sugeno neuro-fuzzy model and the Neural Network 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 Militarised Interstate Disputes (MID) dataset obtained from the correlates of war project. In the this work, the neural network model is trained to predict conflict using the Bayesian framework. It is found that the neural network is able to forecast conflict with an accuracy of 77.30%. Knowledge from the neural network model is then extracted using the Automatic Relevance Determination method and by performing a sensitivity analyis. The Takagi-Sugeno Neuro-fuzzy model is optimised to forecast conflict giving an accuracy 80.36%. Knowledge from the Takagi-Sugeno neuro-fuzzy model is extracted by interpreting the model's fuzzy rules and their outcomes. It is found that both models offer some transparency which helps in understanding conflict management.

Original languageEnglish
Title of host publicationINES 2007 - 11th International Conference on Intelligent Engineering Systems, Proceedings
Pages161-166
Number of pages6
DOIs
Publication statusPublished - 2007
EventINES 2007 - 11th International Conference on Intelligent Engineering Systems - Budapest, Hungary
Duration: 29 Jun 20071 Jul 2007

Publication series

NameINES 2007 - 11th International Conference on Intelligent Engineering Systems, Proceedings

Conference

ConferenceINES 2007 - 11th International Conference on Intelligent Engineering Systems
Country/TerritoryHungary
CityBudapest
Period29/06/071/07/07

ASJC Scopus subject areas

  • Artificial Intelligence
  • Control and Systems Engineering

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