4eMka2

4eMka2 is an implementation of the new approach in multiple criteria decision support, combining advantages of rough sets and dominance relation. The purpose of this system is resolving of multi-criteria sorting problems. System can be used in many different areas e.g. finances, medicine, geology, pharmacology and many other connected with analysis of vast data sets. The main difference between this system and the ones that are already in use is that it bases on rough set theory combined with dominance relation, which is quite new approach in multi-criteria decision support. The main function of the system is extraction of the classification rules from a set of already classified examples. These rules could be used to make partition of new data sets. Rules are presented in very convenient and comprehensible manner as a set of ”if ... then...” sentences. Another advantage of the system is dealing with inconsistent and incomplete data. This is possible due to use of rough set with dominance relation. The role of the user is simplified to preparation of the classified examples set and analysis of induced rules. It means that user stays within range of his domain. He does not have to get familiar with theory basis of used analysis model, which is often case in similar systems e.g. UTA and Electre. These systems do require much more skills from user at least to evaluate some additional coefficients. That means our system should be more user friendly and require less additional time spent to learn.


References in zbMATH (referenced in 53 articles )

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  1. Zaras, Kazimierz: Rough approximation of a preference relation by a multi-attribute dominance for deterministic, stochastic and fuzzy decision problems (2004)
  2. Azibi, R.; Vanderpooten, D.: Aggregation of dispersed consequences for constructing criteria: The evaluation of flood risk reduction strategies (2003)
  3. Sai, Ying; Yao, Y. Y.: Analyzing and mining ordered information tables (2003)
  4. Azibi, R.; Vanderpooten, D.: Construction of rule-based assignment models (2002)
  5. Bouyssou, D.; Pirlot, M.: Nontransitive decomposable conjoint measurement. (2002)
  6. Greco, Salvatore; Matarazzo, Benedetto; Slowinski, Roman: Rough sets methodology for sorting problems in presence of multiple attributes and criteria (2002)
  7. Larichev, Oleg; Asanov, Artyom; Naryzhny, Yevgeny: Effectiveness evaluation of expert classification methods (2002)
  8. Sarkar, Manish: Rough--fuzzy functions in classification (2002)
  9. Tay, Francis E. H.; Shen, Lixiang: Economic and financial prediction using rough sets model (2002)
  10. Zhao, Kai; Wang, Jue: A reduction algorithm meeting users’ requirements. (2002)
  11. Zopounidis, Constantin; Doumpos, Michael: Multicriteria classification and sorting methods: A literature review (2002)
  12. Greco, Salvatore; Matarazzo, Benedetto; Slowinski, Roman: Rough sets theory for multicriteria decision analysis (2001)
  13. Greco, Salvatore; Matarazzo, Benedetto; Slowinski, Roman: The use of rough sets and fuzzy sets in MCDM (1999)