Classifier Fusion Using Dempster-Shafer theory of evidence to Predict Breast Cancer Tumors

Abstract

In classifier fusion models, classifiers outputs are combined to achieve a group decision. The most often used classifiers fusion models are majority vote, probability schemes, weighted averaging and Bayes approach to name few. We propose a model of classifiers fusion by combining the mathematical belief of classifiers. We used Dempster-shafer theory of evidence to determine the mathematical belief of classifiers. Support Vector Machine (SVM) with linear, polynomial and radial kernel has been employed as classifiers. The output of classifiers used as basis for computing beliefs. We combined these beliefs to arrive at one final decision. Our experimental results have shown that the new proposed classifiers fusion methodology have outperforms single classification models.