Method of Basic Belief Assignment Determination Based on Density Estimation of Ambiguous Samples

Abstract

The determination of the basic belief assignment (BBA) is an important yet difficult problem in evidence theory. In this paper, some BBA determination methods using density estimation that can directly generate compound focal elements for ambiguous classes are proposed, including the Gaussian Mixture Model (GMM) based and Generative Adversarial Network (GAN) based methods. Experimental results of evidence combination based pattern classification on various UCI data sets show that our new proposed methods are rational and can effectively improve the accuracy of fusion based pattern classification.