IDEAS home Printed from https://ideas.repec.org/a/wsi/acsxxx/v22y2019i04ns0219525919500073.html
   My bibliography  Save this article

Comparison Of Two Methods For Generating The Coalitions Of Classifiers And Two Methods For Reducing Dimensionality In A Dispersed Decision-Making System

Author

Listed:
  • MAŁGORZATA PRZYBYŁA-KASPEREK

    (Institute of Computer Science, University of Silesia, Bȩdzińska 39, 41-200 Sosnowiec, Poland)

Abstract

In this paper, we consider a system in which knowledge in a dispersed form is available. In the system local classifiers are combined into coalitions. Two methods of combining classifiers in coalitions are discussed in this paper — with a hierarchical agglomeration algorithm and with Pawlak’s conflict model. The purpose of this paper is to apply methods for reducing dimensionality in these two approaches. Two methods of attribute reduction are considered — based on the rough set theory and based on attribute correlation with decision class. The most important conclusions formulated in the paper are as follows. The use of attribute selection method improves the quality of classification of the dispersed system. Better results are generated by the system with a hierarchical agglomeration algorithm.

Suggested Citation

  • Małgorzata Przybyła-Kasperek, 2019. "Comparison Of Two Methods For Generating The Coalitions Of Classifiers And Two Methods For Reducing Dimensionality In A Dispersed Decision-Making System," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 22(04), pages 1-31, June.
    • Handle: RePEc:wsi:acsxxx:v:22:y:2019:i:04:n:s0219525919500073
    DOI: 10.1142/S0219525919500073
    as

    Download full text from publisher

    File URL: http://www.worldscientific.com/doi/abs/10.1142/S0219525919500073
    Download Restriction: Access to full text is restricted to subscribers
    File URL: https://libkey.io/10.1142/S0219525919500073?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Pawlak, Zdzislaw, 2005. "Some remarks on conflict analysis," European Journal of Operational Research, Elsevier, vol. 166(3), pages 649-654, November.
    2. Roman Słowiński & Salvatore Greco & Benedetto Matarazzo, 2014. "Rough-Set-Based Decision Support," Springer Books, in: Edmund K. Burke & Graham Kendall (ed.), Search Methodologies, edition 2, chapter 0, pages 557-609, Springer.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Barbati, Maria & Greco, Salvatore & Kadziński, Miłosz & Słowiński, Roman, 2018. "Optimization of multiple satisfaction levels in portfolio decision analysis," Omega, Elsevier, vol. 78(C), pages 192-204.
    2. Andonegi, Aitor & Garmendia, Eneko & Aldezabal, Arantza, 2021. "Social multi-criteria evaluation for managing biodiversity conservation conflicts," Land Use Policy, Elsevier, vol. 109(C).
    3. Chakhar, Salem & Ishizaka, Alessio & Labib, Ashraf & Saad, Inès, 2016. "Dominance-based rough set approach for group decisions," European Journal of Operational Research, Elsevier, vol. 251(1), pages 206-224.
    4. Yun Kang & Shunxiang Wu & Yuwen Li & Wei Weng, 2017. "New and improved: grey multi-granulation rough sets," International Journal of Systems Science, Taylor & Francis Journals, vol. 48(12), pages 2575-2589, September.
    5. Szeląg, Marcin & Słowiński, Roman, 2024. "Explaining and predicting customer churn by monotonic rules induced from ordinal data," European Journal of Operational Research, Elsevier, vol. 317(2), pages 414-424.
    6. Angilella, Silvia & Corrente, Salvatore & Greco, Salvatore & Słowiński, Roman, 2016. "Robust Ordinal Regression and Stochastic Multiobjective Acceptability Analysis in multiple criteria hierarchy process for the Choquet integral preference model," Omega, Elsevier, vol. 63(C), pages 154-169.
    7. Cinelli, Marco & Kadziński, Miłosz & Gonzalez, Michael & Słowiński, Roman, 2020. "How to support the application of multiple criteria decision analysis? Let us start with a comprehensive taxonomy," Omega, Elsevier, vol. 96(C).
    8. Du, Wen Sheng & Hu, Bao Qing, 2017. "Dominance-based rough fuzzy set approach and its application to rule induction," European Journal of Operational Research, Elsevier, vol. 261(2), pages 690-703.
    9. Przybyła-Kasperek, Małgorzata & Wakulicz-Deja, Alicja, 2016. "The strength of coalition in a dispersed decision support system with negotiations," European Journal of Operational Research, Elsevier, vol. 252(3), pages 947-968.
    10. Junliang Du & Sifeng Liu & Yong Liu & Liangyan Tao, 2023. "Multi-criteria Large-Scale Group Decision-Making in Linguistic Contexts: A Perspective of Conflict Analysis and Resolution," Group Decision and Negotiation, Springer, vol. 32(1), pages 177-207, February.
    11. Małgorzata Przybyła-Kasperek, 2020. "Coalitions’ Weights in a Dispersed System with Pawlak Conflict Model," Group Decision and Negotiation, Springer, vol. 29(3), pages 549-591, June.
    12. Kao-Yi Shen, 2017. "Compromise between Short- and Long-Term Financial Sustainability: A Hybrid Model for Supporting R&D Decisions," Sustainability, MDPI, vol. 9(3), pages 1-17, March.
    13. Marco Cinelli & Matteo Spada & Miłosz Kadziński & Grzegorz Miebs & Peter Burgherr, 2019. "Advancing Hazard Assessment of Energy Accidents in the Natural Gas Sector with Rough Set Theory and Decision Rules," Energies, MDPI, vol. 12(21), pages 1-17, November.
    14. Małgorzata Przybyła-Kasperek, 2019. "Three Conflict Methods in Multiple Classifiers that Use Dispersed Knowledge," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(02), pages 555-599, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wsi:acsxxx:v:22:y:2019:i:04:n:s0219525919500073. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Tai Tone Lim (email available below). General contact details of provider: http://www.worldscinet.com/acs/acs.shtml .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.