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Application of Reduction of the Set of Conditional Attributes in the Process of Global Decision-making

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Języki publikacji
EN
Abstrakty
EN
The paper includes a discussion of issues related to the process of global decision-making on the basis of information stored in several local knowledge bases. The local knowledge bases contain information on the same subject, but are defined on different sets of conditional attributes that are not necessarily disjoint. A decision-making system, which uses a number of knowledge bases, makes global decisions on the basis of a set of conditional attributes specified for all of the local knowledge bases used. The paper contains a description of a multi-agent decision-making system with a hierarchical structure. Additionally, it briefly overviews methods of inference that enable global decision-making in this system and that were proposed in our earlier works. The paper also describes the application of the conditional attributes reduction technique to local knowledge bases. Our main aim was to investigate the effect of attribute reduction on the efficiency of inference in such a system. For a measure of the efficiency of inference, we mean mainly an error rate of classification, for which a definition is given later in this paper. Therefore, our goal was to reduce the error rate of classification.
Wydawca
Rocznik
Strony
327--355
Opis fizyczny
Bibliogr. 34 poz., tab., wykr.
Twórcy
  • Institute of Computer Science University of Silesia Be¸dzi´nska 39, 41-200 Sosnowiec, Poland
  • Institute of Computer Science University of Silesia Be¸dzi´nska 39, 41-200 Sosnowiec, Poland
Bibliografia
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  • [5] Deja, R.: Conflict analysis, Rough Sets; New Developments. In: Polkowski, L. (eds.) Studies in Fussiness and Soft Computer Science, Physica-Verlag (2000)
  • [6] Chen, Y., Garcia, E.K., Gupta, M.R., Rahimi, A., Cazzanti, L.: Similarity-based classification: Concepts and algorithms. The Journal of Machine Learning Research, 10, 747-776 (2009)
  • [7] Delimata, P., Suraj, Z.: Feature Selection Algorithm for Multiple Classifier Systems: A Hybrid Approach. Fundamenta Informaticae 85 (1-4), IOS Press, Amsterdam, 97–110 (2008)
  • [8] Dietterich, T.G.: An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization. Machine Learning, 40(2), 139–157 (2000)
  • [9] Ester, M., Kriegel, H., Sander, J., Xu, X.: A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. Proceedings of 2nd International Conference on Knowledge Discovery and Data Mining, 226–231, Portland (1996)
  • [10] Gatnar, E.: Multiple-model approach to classification and regression. PWN, Warsaw (2008)
  • [11] Grzymała-Busse, J., Rza¸sa, W.: Local and Global Approximations for Incomplete Data. In: J.F. Peters, A. Skowron (Eds.) Transactions on Rough Sets VIII, Lecture Notes of Computer Sciences 5084, Springer-Verlag, Berlin, 21–34 (2008)
  • [12] Grzymała-Busse, J., Grzymała-Busse, W., Hippe, Z., Rząsa, W.: An Improved Comparison of Three Rough Set Approaches to Missing Attribute Values. In Control and Cybernetics, Vol 39, No 2, 469–486 (2010)
  • [13] Hart, P.: The condensed nearest neighbor rule. IEEE Transactions on Information Theory 16, 515–516 (1968)
  • [14] Jiang, W., Zhang, X., Cohen, A., Ras, Z.: Multiple Classifiers for Different Features in Timbre Estimation. Advances in Intelligent Information Systems, 335–356 (2010)
  • [15] Kargupta, H., Park, B., Johnson, E., Sanseverino, E., Silvestre, L., Hershberger, D.: Collective Data Mining From Distributed Vertically Partitioned Feature Space. In Workshop on distributed data mining. International Conference on Knowledge Discovery and Data Mining (1998)
  • [16] Koronacki, J., C´wik, J.: Statistical learning systems. EXIT, Warsaw (2008)
  • [17] Kuncheva, L.: Combining pattern classifiers methods and algorithms. John Wiley & Sons (2004)
  • [18] Michalski, R., Wojtusiak, J.: The Distribution Approximation Approach to Learning from Aggregated Data. Reports of the Machine Learning and Inference Laboratory, MLI 08-2, George Mason University, Fairfax, VA (2008)
  • [19] Pawlak, Z.: An Inquiry Anatomy of Conflicts. Journal of Information Sciences 109, 65–78 (1998)
  • [20] Pawlak, Z.: On conflicts. Int. J. of Man-Machine Studies 21, 127–134 (1984)
  • [21] Pawlak, Z.: Rough sets. International Journal of Information & Computer Sciences 11, 341–356 (1982)
  • [22] Pawlak, Z.: Rough Sets: Theoretical aspects of reasoning about data. Kluwer Academic Publishers, Boston (1991)
  • [23] Skowron, A., Deja, R.: On Some Conflict Models and Conflict Resolutions. Romanian Journal of Information Science and technology 3(1-2), 69–82 (2002)
  • [24] Skowron, A., Wang, H., Wojna, A., Bazan, J.: Multimodal Classification: Case Studies. T. Rough Sets, 224–239 (2006)
  • [25] Straffin, P.: Game Theory and Strategy. Mathematical Association of America, New York (1993)
  • [26] Suraj, Z., Gayar, Neamat El, Delimata, P.: A Rough Set Approach to Multiple Classifier Systems. Fundamenta Informaticae 72 (1-3), IOS Press, Amsterdam, 393–406 (2006)
  • [27] Wakulicz-Deja, A., Przybyła-Kasperek, M.: Hierarchical Multi-Agent System. Recent Advances in Intelligent Information Systems, Academic Publishing House EXIT, 615–628 (2009)
  • [28] Wakulicz-Deja, A., Przybyła-Kasperek, M.: Global decisions Taking on the Basis of Multi-Agent System with a Hierarchical Structure and Density-Based Algorithm. Concurrency, Specification and Programming CS&P 2009, Uniwersytet Warszawski, 616–627 (2009)
  • [29] Wakulicz-Deja, A., Przybyła-Kasperek, M.: Multi-Agent Decision Taking System. Fundamenta Informaticae 101(1-2), 125–141 (2010)
  • [30] Wakulicz-Deja, A., Przybyła-Kasperek, M.: Multi-agent decision-making system - comparison of methods. ZN Pol. ´ Sl. Studia Informatica 31, 2A (89), 173–188 (2010)
  • [31] Przybyła-Kasperek, M., Multi-agent decision-making system - conflicts analysis. PhD thesis, Supervisor: Wakulicz-Deja A. (2010)
  • [32] 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)
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Typ dokumentu
Bibliografia
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