Conflict modelling and knowledge extraction using computational intelligence methods

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.