Decision fusion using fuzzy dempster-shafer theory

Abstract

One of the popular tools in decision making is a decision fusion since there might be several sources that provide decisions for one task. The Dempster’s rule of combination is one of the decision fusion methods used frequently in many research areas. However, there are so many uncertainties in classifier output. Hence, we propose a fuzzy Dempster’s rule of combination (FDST) where we fuzzify the discounted basic probability assignment and compute the fuzzy combination. We also have a rejection criterion for any sample with higher belief in both classes, not only one of the classes. We run the experiment with 2 classifiers, i.e., support vector machine (SVM) and radial basis function (RBF). We test our algorithm on 5 data sets from the UCI machine learning repository and SAR images on three military vehicle types. We compare our fusion result with that from the regular Dempster’s rule of combination (DST). All of our results are comparable or better than those from the DST.