Global decision-making system with dynamically generated clusters

• New approach to the organization of dispersed decision system structure was proposed.
• The local knowledge bases, which are in a friendship relation, are grouped.
• The groups of local knowledge bases will be generated in a dynamic way.
• Process of elimination inconsistencies in knowledge are implemented in groups.
• Global decisions are made by using one of the methods for analysis of conflicts.

This paper discusses the issues 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). In the paper a decision-making system is described. In this system, the classification process of the test object can be divided into several steps. In the first step, we investigate how particular classifiers classify a test object. We describe this using probability vectors over decision classes. We cluster classifiers with respect to similarities of the probability vectors. 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 clusters.The paper proposes a new approach to the organization of the structure of a decision-making system, which operates on the basis of dispersed knowledge. In the presented system, the classifiers are combined into groups called clusters in a dynamic way. We seek to designate groups of classifiers that classify the test object in a similar manner. The groups of classifiers are not disjoint sets. We use overlapping clusters because this is a more suitable representation of classification compatibility. It is assumed that, if the classifier classifies the test object in an ambiguous way, it should belong to several clusters. Then, a process of the elimination of inconsistencies in the knowledge is implemented in the created groups. Global decisions are made by using one of the methods for the analysis of conflicts.