Abstract
This paper proposes a method to optimize automatically the structure and parameters of fuzzy (Choquet) integral models using the Bayesian framework simultaneously. It is achieved by introducing the concepts of dependency and independency of fuzzy measure in order to judge the redundancy of fuzzy measure, defining the interactive measure in fuzzy measure, and representing the Choquet integral model by interactive measure. Applying this method to numerical experiments, we proved the effectiveness of the optimization of the Choquet integral model by means of Log evidence, interactive measure and its identifying method, and the optimization of parameters by means of the significance evaluation index for interactive measure.
This method enables to realize more objective representation of the ordering of the significance of fuzzy measure and more rapid optimization of fuzzy integral models which is hardly affected by errors and can increase the range of its use than conventional methods.
