Full-text resources of CEJSH and other databases are now available in the new Library of Science.
Visit https://bibliotekanauki.pl

PL EN


2019 | 14 | 144-156

Article title

Identifying Strategic Development Objectives for European Union States Using the Dominance-Based Rough Set Approach: The Case of Poland

Content

Title variants

Languages of publication

EN

Abstracts

EN
The use of the dominance-based rough set approach (DRSA) to help identify and prioritize strategic political, economic, sociological and technological (PEST) objectives for European Union (EU) countries is presented. The countries are first grouped into three categories: [A] those that are doing well according to the selected indicators; [B] those that need support to acquire category A status; [C] those ranked the lowest and needing special support with regard to the criteria considered. The categories correspond to tertiles within the average ranking of all EU countries. DRSA then provides decision rules based on PEST needs in order to improve the development and classification of the country. We conclude that by using this methodology, the EU could identify the strategic objectives to be given priority in order to stimulate its economic development or to improve the economic and sociological status of any country in the union. The case of Poland, a category C country from an economic perspective, is of particular interest.

Year

Volume

14

Pages

144-156

Physical description

Contributors

  • Université du Québec en Abitibi-Témiscamingue. Rouyn-Noranda, Canada
  • Université du Québec en Abitibi-Témiscamingue. Rouyn-Noranda, Canada
author
  • Université du Québec en Abitibi-Témiscamingue. Rouyn-Noranda, Canada

References

  • Emam O., Farhan M., Abohany A. (2017), Faults Repairing Analysis Using Rough Sets after Implementation of Labor Force Redistribution Algorithm: A Case Study in Telecom Egypt, Information Sciences Letter, 6(3), 39-48.
  • Field A. (2005), Discovering Statistics Using SPSS, 2nd Edition, SAGE Publications, New Delhi.
  • Greco S., Matarazzo B., Slowinski R. (1999), The Use of Rough Sets and Fuzzy Sets in MCDM, [in:] T. Gal, T. Hanne, T. Stewart (eds.), Advances in Multiple Criteria Decision Making, Kluwer Academic Publishers, Dordrecht, Boston, 14.1-14.59.
  • Greco S., Matarazzo B., Slowinski R. (2001), Rough Sets Theory for MultiCriteria Decision Analysis, European Journal of Operational Research, 129, 1-47.
  • Ho H.-Ch., Fann W.J.-D., Chiang H.-J., Nguyen P.-T., Pham D.-H., Nguyen P.-H., Nagai M. (2016), Application of Rough Set, GSM and MSM to Analyze Learning Outcome An Example of Introduction to Education, Journal of Intelligent Learning Systems and Applications, 8, 23-38.
  • International Institute for Strategic Studies, IISS, viewed 4 January 2018, https://www.iiss.org
  • Marin J.-C., Zaras K., Boudreau-Trudel B. (2014), Use of the Dominance-Based Rough Set Approach as a Decision Aid Tool for the Selection of Development Projects in Northern Quebec, Modern Economy, 5, 723-741.
  • Pawlak Z. (1982), Rough Set, International Journal of Parallel Programming, 11, 341-356.
  • Pawlak Z. (1991), Rough Sets: Theoretical Aspects of Reasoning about Data, Kluwer Academic Publishing, Dordrecht, http://dx.doi.org/10.1007/978-94-011-3534-4
  • Pawlak Z. (2002), Rough Set Theory and Its Applications, Journal of Telecommunications and Information Theory, 3, 7-10.
  • Pawlak Z., Slowinski R. (1994), Rough Set Approach to Multi-Attribute Decision Analysis, European Journal of Operational Research, 72, 443-459, http://dx.doi.org/10.1016/0377-2217(94)90415-4
  • Prema S., Umamaheswari P. (2016), Multitude Classifier Using Rough Set Jelinek-Mercer Naïve Bayes for Disease Diagnosis, Circuits and Systems, 7, 701-708.
  • Renaud J., Thibault J., Lanouette R., Kiss L.N., Zaras K., Fonteix C. (2007), Comparison of Two Multi-Criteria Methods: Net Flow and Rough Set Methods for Aid to Decision Making in a High Yield Pulping Process, European Journal of Operational Research, 177(3), 1418-1432.
  • Songbian Z. (2016), Business Intelligence from Customer Review Management Using Rough Set Model, International Journal of Advanced Research, 4, 816-824.
  • United Nations 2018, UNData, viewed 4 January 2018, http://data.un.org/Explorer.aspx?d=UNODC
  • World Bank 2018, Indicators, viewed 4 January 2018. https://data.worldbank.org/indicator.
  • Zaras K. (2004), Rough Approximation of a Preference Relation by a Multi-attribute Stochastic Dominance for Deterministic, Stochastic and Fuzzy Evaluation Problems, European Journal of Operational Research, 159, 196-206.
  • Zaras K., Marin J.-C., Boudreau-Trudel B. (2012), Dominance Rough Set Approach as a Decision-Making Method for the Selection of Sustainable Development Projects, American Journal of Operational Research, 2, 506.

Document Type

Publication order reference

Identifiers

ISSN
2084-1531

YADDA identifier

bwmeta1.element.cejsh-56c59461-74d9-4d63-949f-0ff37627e088
JavaScript is turned off in your web browser. Turn it on to take full advantage of this site, then refresh the page.