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Attribute Selection in a Dispersed Decision-Making System

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Advances in Feature Selection for Data and Pattern Recognition

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 138))

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Abstract

In this chapter, the use of a method for attribute selection in a dispersed decision-making system is discussed. Dispersed knowledge is understood to be the knowledge that is stored in the form of several decision tables. Different methods for solving the problem of classification based on dispersed knowledge are considered. In the first method, a static structure of the system is used. In more advanced techniques, a dynamic structure is applied. Different types of dynamic structures are analyzed: a dynamic structure with disjoint clusters, a dynamic structure with inseparable clusters and a dynamic structure with negotiations. A method for attribute selection, which is based on the rough set theory, is used in all of the methods described here. The results obtained for five data sets from the UCI Repository are compared and some conclusions are drawn.

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Authors and Affiliations

  1. Institute of Computer Science, University of Silesia in Katowice, Bȩdzińska 39, 41-200, Sosnowiec, Poland

    Małgorzata Przybyła-Kasperek

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  1. Małgorzata Przybyła-Kasperek
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Correspondence to Małgorzata Przybyła-Kasperek .

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Editors and Affiliations

  1. Silesian University of Technology , Gliwice, Poland

    Urszula Stańczyk

  2. University of Silesia in Katowice , Katowice, Poland

    Beata Zielosko

  3. University of Bournemouth , Poole, United Kingdom

    Lakhmi C. Jain

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Przybyła-Kasperek, M. (2018). Attribute Selection in a Dispersed Decision-Making System. In: Stańczyk, U., Zielosko, B., Jain, L. (eds) Advances in Feature Selection for Data and Pattern Recognition. Intelligent Systems Reference Library, vol 138. Springer, Cham. https://doi.org/10.1007/978-3-319-67588-6_8

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  • DOI: https://doi.org/10.1007/978-3-319-67588-6_8

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