Dispersed Decision-making System Research Articles

Dispersed decision-making system with fusion methods from the rank level and the measurement level – A comparative study

This article discusses the problem of decision-making based on dispersed knowledge that is stored in several independent knowledge bases. The dispersed decision-making system, which was proposed in a previous paper of the authors, is used. In this study, four fusion methods from the rank level and nine methods from the measurement level were used in this dispersed system. These methods were tested on three data sets from the UCI Repository – Soybean, Vehicle Silhouettes and Landsat Satellite. The sets are diverse in terms of the number of objects, the number of conditional attributes and the number of decision classes. There are also various types of conditional attributes in these sets. The experimental section is divided according to the three objectives of the article. The fusion methods were compared in the two groups – rank and measurement levels. In addition, experiments were carried out fusing multiple methods simultaneously in the decision-making process. Methods from the rank level and the measurement level were applied simultaneously in the same decision-making process. Then the decisions that were generated by the methods were merged. The results were compared and conclusions were drawn. The third goal of the article was to compare the efficiency of the inference of fusion method with and without the use of a dispersed system. It was found that the use of a dispersed system improved the efficiency of inference in most cases.

Comparison of fusion methods from the abstract level and the rank level in a dispersed decision-making system

Issues related to decision making based on dispersed knowledge are discussed in the paper. A dispersed decision-making system, which was proposed by the authors in previous articles, is used in this paper. In the system, a process of combining classifiers into coalitions with a negotiation stage is realized. The novelty that is proposed in this article involves the use of six different methods of conflict analysis that are known from the literature.The main purpose of the tests, which were performed, was to compare the methods from the two groups – the abstract level and the rank level. An additional aim was to investigate the efficiency of the fusion methods used in a dispersed system with a dynamic structure with the efficiency that is obtained when no structure is used. Conclusions were drawn that, in most cases, the use of a dispersed system improves the efficiency of inference.

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.

A dispersed decision-making system – The use of negotiations during the dynamic generation of a system’s structure

The issues that are related to the process of global decision-making on the basis of knowledge which is stored in a dispersed form (several local knowledge bases or classifiers) are discussed in this paper. In a decision-making system, which is described in the paper, the classification process of the test object starts with an investigation of how particular classifiers classify a test object. We describe the views of classifiers by using probability vectors over decision classes. In the system, the process of combining classifiers in coalitions is very important. Negotiation is used in the clustering process. We define three types of relations between classifiers: friendship, conflict and neutrality. The clustering process consists of two stages. In the first step, the initial groups are created. These groups contain classifiers that are in a friendship relation. In the second stage, classifiers which are in neutrality relation are attached to the existing groups. In this paper, a formal description of the clustering process is presented and mathematical properties of functions, which are used, are described. For every cluster, we find a kind of combined information. Finally, we classify the given test object by voting among clusters, using the combined information from each of the clusters.In the paper a new way of creating clusters (with a negotiation stage) is compared to the approach presented in the paper (Przybyła-Kasperek and Wakulicz-Deja, 2014) [23] (without negotiations). There are significant differences between the clusters that are generated using these two approaches, which are shown in the paper. In the new approach, the clusters are more complex and better reconstruct and illustrate the views of the classifiers on the classification.