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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Bazan, J.G., Peters, J.F., Skowron, A., Nguyen, H.S., Szczuka, M.S.: Rough set approach to pattern extraction from classifiers. Electr. Notes Theor. Comput. Sci. 82(4), 20–29 (2003)
Clark, P., Niblett, T.: Induction in noisy domains. In: EWSL, pp. 11–30 (1987)
Delimata, P., Suraj, Z.: Feature selection algorithm for multiple classifier systems: A hybrid approach. Fundam. Inform. 85(1-4), 97–110 (2008)
Michalski, R.S., Mozetic, I., Hong, J., Lavrac, N.: The multi-purpose incremental learning system AQ15 and its testing application to three medical domains. In: AAAI, pp. 1041–1047 (1986)
Pawlak, Z.: On conflicts. International Journal of Man-Machine Studies 21(2), 127–134 (1984)
Pawlak, Z.: An inquiry into anatomy of conflicts. Inf. Sci. 109(1-4), 65–78 (1998)
Przybyla-Kasperek, M.: Wieloagentowy system decyzyjny z dynamicznie generowanymi rozlacznymi klastrami. ZN Pol. Sl. Studia Informatica 34(2A(111)), 275–294 (2013)
Przybyla-Kasperek, M., Wakulicz-Deja, A.: Application of reduction of the set of conditional attributes in the process of global decision-making. Fundam. Inform. 122(4), 327–355 (2013)
Przybyła-Kasperek, M., Wakulicz-Deja, A.: Global decisions taking on the basis of dispersed medical data. In: Ciucci, D., Inuiguchi, M., Yao, Y., Ślęzak, D., Wang, G. (eds.) RSFDGrC 2013. LNCS, vol. 8170, pp. 355–365. Springer, Heidelberg (2013)
Schneeweiss, C.: Distributed decision making. Springer, Berlin (2003)
Skowron, A., Wang, H., Wojna, A., Bazan, J.: Multimodal classification: Case studies. In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets V. LNCS, vol. 4100, pp. 224–239. Springer, Heidelberg (2006)
Slezak, D., Wroblewski, J., Szczuka, M.S.: Neural network architecture for synthesis of the probabilistic rule based classifiers. Electr. Notes Theor. Comput. Sci. 82(4), 251–262 (2003)
Wakulicz-Deja, A., Przybyla-Kasperek, M.: Application of the method of editing and condensing in the process of global decision-making. Fundam. Inform. 106(1), 93–117 (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-06932-6_28
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-06931-9
Online ISBN: 978-3-319-06932-6
eBook Packages: Computer ScienceComputer Science (R0)