Selected Methods of Combining Classifiers, when Predictions Are Stored in Probability Vectors, in a Dispersed Decision-Making System

Issues that are related to decision making that is based on dispersed knowledge are discussed in the paper. A system, that was proposed in the article [12], is used in this paper. In the system the process of combining classifiers in coalitions is very important and negotiation is applied in the clustering process. The main aim of the article is to compare the results obtained using five different methods of conflict analysis in the system. All these methods are used if the individual classifiers generate probability vectors over decision classes. The most popular methods are considered: a sum, a product, a median, a maximum and a minimum rules. In the paper, tests, which were performed on data from the UCI repository, are presented. The best methods in a particular situation are indicated. It was found out that some methods do not generate satisfactory results when there are dummy agents in a dispersed data set. That is there are undecided agents who assign the same probability value to many different decision values.