Abstract
The main aim of the article is to present a decision-making system using dispersed knowledge. The article introduces the system with dynamically generated coalitions. The local knowledge bases, on the basis of which a similar classification for the test object is made, are combined into a coalition. In the proposed system, the classification process can be divided into several steps. In the first step we describe the classification of a test object made on the basis of local knowledge base, by probability vectors over decision classes. We cluster local knowledge bases with respect to similarities of probability vectors. For every cluster, we find a kind of combined information. Finally, we classify the test object using the method for the conflict analysis. The main aim of the paper is to present the results of experiments on medical data. 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.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Bazan, J., Peters, J., Skowron, A., Nguyen, H., Szczuka, M.: Rough set approach to pattern extraction from classifiers. In: Electronic Notes in Theoretical Computer Science, vol. 82, Elsevier Science Publishers (2003)
Clark, P., Niblett, T.: Induction in Noisy Domains. In: Bratko, I., Lavrac, N. (eds.) Progress in Machine Learning, pp. 11–30 (1987)
Delimata, P., Suraj, Z.: Feature Selection Algorithm for Multiple Classifier Systems: A Hybrid Approach. Fundamenta Informaticae 85(1-4), 97–110 (2008)
Michalski, R., Mozetic, I., Hong, J., Lavrac, N.: The Multi-Purpose Incremental Learning System AQ15 and its Testing Applications to Three Medical Domains. In: Proceedings of the Fifth National Conference on Artificial Intelligence, pp. 1041–1045 (1986)
Nowak-Brzezińska, A., Simiński, R.: Knowledge mining approach for optimization of inference processes in medical rule knowledge bases. J. of Medical Infor. & Tech. 20, 19–27 (2012)
Marszał-Paszek, B., Paszek, P.: Nondeterministic decision rules in classification process for medical data. J. of Medical Infor. & Tech. 17, 59–64 (2011)
Pawlak, Z.: On conflicts. Int. J. of Man-Machine Studies 21, 127–134 (1984)
Pawlak, Z.: An Inquiry Anatomy of Conflicts. Journal of Information Sciences 109, 65–78 (1998)
Przybyła-Kasperek, M., Wakulicz-Deja, A.: Application of reduction of the set of conditional attributes in the process of global decision-making. Fundamenta Informaticae 122(4), 327–355 (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)
Ślęzak, D., Wróblewski, J., Szczuka, M.: Neural network architecture for synthesis of the probabilistic rule based classifiers. In: Electronic Notes in Theoretical Computer Science, vol. 82. Elsevier (2003)
Wakulicz-Deja, A., Przybyła-Kasperek, M.: Multi-Agent Decision Taking System. Fundamenta Informaticae 101(1-2), 125–141 (2010)
Wakulicz-Deja, A., Przybyła-Kasperek, M.: Application of the method of editing and condensing in the process of global decision-making. Fundamenta Informaticae 106(1), 93–117 (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Przybyła-Kasperek, M., Wakulicz-Deja, A. (2013). Global Decisions Taking on the Basis of Dispersed Medical Data. In: Ciucci, D., Inuiguchi, M., Yao, Y., Ślęzak, D., Wang, G. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. RSFDGrC 2013. Lecture Notes in Computer Science(), vol 8170. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41218-9_38
Download citation
DOI: https://doi.org/10.1007/978-3-642-41218-9_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-41217-2
Online ISBN: 978-3-642-41218-9
eBook Packages: Computer ScienceComputer Science (R0)