Abstract
The article discusses the issues related to the decision-making system using dispersed knowledge. In the proposed system, the classification process of the test object can be described as follows. 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. In the paper a new approach has been proposed in which the clustering process consists of two stages and three types of relations between classifiers: friendship, conflict and neutrality are defined. In the first step initial groups are created. Such a group contains classifiers that are in friendship relation. In the second stage, classifiers which are in neutrality relation are attached to the existing groups. In experiments the situation is considered in which medical data from one domain are collected in many medical centers. We want to use all of the collected data at the same time in order to make a global decisions.
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Przybyła-Kasperek, M. (2014). Global Decisions Taking Process, Including the Stage of Negotiation, on the Basis of Dispersed Medical Data. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B., Kostrzewa, D. (eds) Beyond Databases, Architectures, and Structures. BDAS 2014. Communications in Computer and Information Science, vol 424. Springer, Cham. https://doi.org/10.1007/978-3-319-06932-6_28
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DOI: https://doi.org/10.1007/978-3-319-06932-6_28
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