Comparison of four methods of combining classifiers on the basis of dispersed medical data

Abstract

The main aim of the article is to compare the results obtained using four different methods of combining classifiers in a dispersed decision-making system. In the article the following fusion methods are used: the majority vote, the weighted majority vote, the Borda count method and the highest rank method. Two of these methods are used if the individual classifier generates a class label and two are used in the case when the individual classifier produces ranking of classes instead of unique class choice. All of these methods were tested in a situation when we have access to data from medical field and this data are in a dispersed form. The use of dispersed medical data is very important because it is common situation that medical data from one domain are collected in many different medical centers. It would be good to be able to use all this accumulated knowledge at the same time.