Logo PTI
Polish Information Processing Society
Logo FedCSIS

Annals of Computer Science and Information Systems, Volume 8

Proceedings of the 2016 Federated Conference on Computer Science and Information Systems

Dispersed decision-making system with selected fusion methods from the measurement level - case study with medical data

DOI: http://dx.doi.org/10.15439/2016F30

Citation: Proceedings of the 2016 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 8, pages 129–136 ()

Full text

Abstract. In the paper issues related to the use of dispersed knowledge in medicine are discussed. The main aim of the article is to investigate the efficiency of inference of seven selected fusion methods in a dispersed decision-making system. The dispersed system was proposed by the author in previous papers. The examined fusion methods - the maximum rule, the minimum rule, the median rule, the sum rule, the probabilistic product method, the method that is based on the theory of evidence and the method that is based on decision templates - are well known from the literature. In the paper two medical data sets from the UCI repository were used. Based on the obtained results it was concluded that for one data set the maximum rule generates the best results, and for other data set better methods are the sum rule and the median rule.

References

  1. Gatnar, E.: Multiple-model approach to classification and regression. PWN, Warsaw, 2008 (in Polish)
  2. Jakubczyc, J., Owoc, M.: Support of Contextual Classifier Ensemble Building, Proceedings of the 2015 Federated Conference on Computer Science and Information Systems, 2015, pp. 1683–1689, http://dx.doi.org/10.15439/2015F353
  3. Kalisch, M.: Supervised Context Classification Methods for an Industrial Machinery, Proceedings of the 2015 Federated Conference on Computer Science and Information Systems, 2015, pp. 1667–1672, http://dx.doi.org/10.15439/2015F292
  4. Kittler, J., Hatef, M., Duin, R.P.W., Matas, J.: On combining classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(3), 1998, pp. 226–239, http://dx.doi.org/10.1109/34.667881
  5. Kuncheva, L., Bezdek, J.C., Duin, R.P.W.: Decision templates for multiple classifier fusion: an experimental comparison. Pattern Recognition, 34(2), 2001, pp. 299–314, http://dx.doi.org/10.1016/S0031-3203(99)00223-X
  6. Kuncheva, L.: Combining pattern classifiers methods and algorithms. John Wiley & Sons, 2004.
  7. Littlestone, N., Warmuth, M.: The Weighted Majority Algorithm. Inf. Comput., 108(2), 1994, pp. 212–261, http://dx.doi.org/10.1006/inco.1994.1009
  8. Przybyła-Kasperek, M., Wakulicz-Deja, A.: Global decisions taking on the basis of dispersed medical data, Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, Lecture Notes in Computer Science Volume 8170, 2013, pp. 355-365, http://dx.doi.org/10.1007/978-3-642-41218-9_38
  9. Przybyła-Kasperek, M., Wakulicz-Deja, A.: Global decision-making system with dynamically generated clusters. Information Sciences, 270, 2014, pp. 172–191, http://dx.doi.org/10.1016/j.ins.2014.02.076
  10. Przybyła-Kasperek, M., Wakulicz-Deja, A.: A dispersed decision-making system—The use of negotiations during the dynamic generation of a systems structure. Information Sciences, 288, 2014, pp. 194–219, http://dx.doi.org/10.1016/j.ins.2014.07.032
  11. Przybyła-Kasperek, M.: Global Decisions Taking Process, Including the Stage of Negotiation, on the Basis of Dispersed Medical Data, S. Kozielski et al. (Eds.): BDAS 2014, CCIS Communica- tions in Computer and Information Science 424, 2014, pp. 290–299, http://dx.doi.org/10.1007/978-3-319-06932-6_28
  12. Przybyła-Kasperek, M.: The Borda Count, the Intersection and the Highest Rank Method in a Dispersed Decision-Making System. Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - 15th International Conference, RSFDGrC 2015, Tianjin, China, November 20-23, 2015, Proceedings, Lecture Notes in Computer Science, 2015, pp. 298–309, http://dx.doi.org/10.1007/978-3-319-25783-9_27
  13. Przybyła-Kasperek, M., Wakulicz-Deja, A.: Global decision-making in multi-agent decision-making system with dynamically generated disjoint clusters. Applied Soft Computing, 40, 2016, pp. 603–615, http://dx.doi.org/10.1016/j.asoc.2015.12.016
  14. Przybyła-Kasperek, M., Wakulicz-Deja, A.: The strength of coalition in a dispersed decision support system with negotiations. European Journal of Operational Research, 2016, pp. 947–968, http://dx.doi.org/10.1016/j.ejor.2016.02.008
  15. Rogova, G. L.: Combining the results of several neural network classifiers. Neural Networks, 7(5), 1994, pp. 777–781, http://dx.doi.org/10.1016/0893-6080(94)90099-X
  16. Tax, D.M.J., Duin, R.P.W., Breukelen, M.: Comparison between product and mean classifier combination rules. In Proc. Workshop on Statistical Pattern Recognition, Prague, Czech, 1997.
  17. Zagorecki, A.: Feature Selection for Naive Bayesian Network Ensemble using Evolutionary Algorithms, Proceedings of the 2014 Federated Conference on Computer Science and Information Systems, 2014, pp. 381–385, http://dx.doi.org/10.15439/2014F498